Medium Access Control Protocols in Cognitive Radio Networks: Overview and General Classification

2092 IEEE COMMUNICATION SURVEYS & TUTORIALS, VOL. 16, NO. 4, FOURTH QUARTER 2014

Medium Access Control Protocols in Cognitive RadioNetworks: Overview and General Classification

Liljana Gavrilovska, Senior Member, IEEE, Daniel Denkovski, Student Member, IEEE,Valentin Rakovic, Student Member, IEEE, and Marko Angjelichinoski

Abstract—The Cognitive Radio and the Cognitive Radio Net-works have recently become one of the most intensively stud-ied paradigms in wireless communications. The main distinctivecharacteristic with respect to the radio environmental conditionsin which the Cognitive Radio Network operates is the time/frequency/space-dependent availability of the spectral resources,a phenomenon commonly referred as spectrum hete-rogeneity.This phenomenon imposes redefinition of the protocol stack byintroducing new communication protocols for the Cognitive RadioNetwork including new Medium Access Control protocols. TheCognitive MAC (C-MAC) protocols employ number of mecha-nisms that address spectrum heterogeneity and provide technicalstrategies for smart exploitation of spectrum’s current underuti-lized time/space/frequency regions that enables large spectrumefficiency gains while maximizing the transparency of the Cog-nitive Radio Networks to the primary system. The classificationand systematization of the existing C-MAC proposals is a complextask due to many C-MAC related aspects. This survey introducesand develops generic, modular and easily extensible layout forclassification and systematization of C-MAC protocols referred asC-MAC cycle. Each C-MAC protocol can be easily fragmented,mapped and visualized using the C-MAC cycle, regardless ofthe operational scenario and settings. The survey offers extensiveoverview on the state-of-the-art advances in C-MAC protocolengineering by reviewing existing and up-to-date technical solu-tions, identifies their basic characteristics and maps them into theC-MAC cycle. The survey also highlights the role of regulative andstandardization activities on C-MAC cycle.

Index Terms—Cognitive radio, medium access control, C-MACcycle, spectrum sensing, spectrum sharing, control channelmanagement.

I. INTRODUCTION

THE Cognitive Radio (CR) and its related concepts andapplications are expected to improve the spectrum utiliza-

tion efficiency and provide solution to the spectrum scarcityproblem. Since the introduction of the CR in [1], it contin-uously attracts a lot of interest in both research communityand industry, making the CR and the Cognitive Radio Net-works (CRNs) one of the most intensively studied paradigms incontemporary wireless communications. In parallel, there have

Manuscript received May 22, 2013; revised January 11, 2014; acceptedApril 3, 2014. Date of publication May 9, 2014; date of current versionNovember 18, 2014.

The authors are with the Faculty of Electrical Engineering and InformationTechnologies, Ss. Cyril and Methodius University in Skopje, 1000 Skopje,Macedonia (e-mail: [email protected]; [email protected];[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/COMST.2014.2322971

been many efforts from various national regulatory agenciesto introduce flexible spectrum usage regulations and novellicensing schemes that enable spectrum sharing, coexistenceand provide support for efficient operation of the CRN inlicensed and unlicensed spectrum [2], [3]. Moreover, someenvisioned applications already have entered the process ofstandardization. Prominent examples of wireless standards thattake advantage of recent developments in the area of cognitiveradio and related spectrum regulations are the following: IEEE802.22 WRAN standard [4] (envisioned to provide wirelessbroadband services in the TV band, i.e. the TV White Spaces)and its two amendments, IEEE 802.22a and IEEE 802.22b [5];IEEE 802.11af standard [6] and its amendments (envisionedto enable legacy 802.11 WLAN services in the TV WhiteSpaces); IEEE 1900.x series of standards [7] (which aim todefine standardized framework for radio resource managementin future wireless systems); IEEE 802.19 [8] (designated todefine coexistence framework for several unlicensed systemssuch as 802.11af, 802.22 and 802.15.4m); and notably, ETSI’slatest efforts towards Licensed Shared Access (LSA) for theLTE mobile operators between 2.3 GHz and 2.4 GHz [9].

The main distinctive characteristic considering radio envi-ronmental conditions where CRNs (i.e. the secondary systems)operate is the variable availability of spectrum resources intime, space and frequency [10]. This phenomenon is referredas spectrum heterogeneity and its behavior (i.e. its dynamics,predictability etc.) is the main factor that determines the sec-ondary system performance, level of protection of the licensedwireless network (i.e. the primary system) and overall spectrumefficiency gain. The spectrally heterogeneous radio environ-ment imposes redefinition of the protocol stack by introducingnew CRN-specific communication protocols. In particular, thephysical layer (PHY) requires some major modifications (e.g.support for multiple air interfaces and a reconfigurable hard-ware for dynamic and fast reconfiguration and adaptation) andadditional functionalities (e.g. spectrum sensing). The CRNsalso require new Medium Access Control (MAC) protocolsdesigned specifically for spectrally heterogeneous environment.The novel Cognitive MAC protocols should employ mecha-nisms for smart exploitation of spectrum’s current underutilizedtime/space/frequency regions. Moreover, the spectrum hetero-geneity due to primary system activity may cause variablesecondary system topology. This fosters definition of newrouting protocols that dynamically adapt to variable networktopology and provide fast route recovery resulting in redesignof the network layer [11]. Additionally, cross layering and tightoperational coupling between the layers can achieve higher

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GAVRILOVSKA et al.: MEDIUM ACCESS CONTROL PROTOCOLS IN COGNITIVE RADIO NETWORKS 2093

TABLE ICOMPARISON OF LEGACY MAC AND C-MAC PROTOCOLS

Fig. 1. C-MAC protocol functional requirements.

secondary system performances providing cross layering to be-come necessity in spectrally heterogeneous environments andinherent feature of the wireless communication system [12].

This survey focuses on MAC protocols designed for CRNsi.e. Cognitive MAC (C-MAC) protocols, their overall impor-tance for efficient operation of CRN, basic features with respectto the operational settings and main challenges concerningthe design of C-MAC protocols that enable large spectrumefficiency gains while providing high level of protection andtransparency for the primary system. Table I compares severalgeneral aspects between C-MAC protocols and legacy MACprotocols. The highlighted general aspects (Table I) and latestresearch advances in CR technology [10], [13]–[15] extract thefollowing multidimensional and conflicting functional require-ments that each C-MAC protocol should address (Fig. 1):

• Secondary-to-primary system transparency. The operationof the secondary system in the licensed bands shouldnot disrupt the operation of the primary system, i.e. theoperation of the secondary system should be as harmlessas possible to the primary system. This could be accom-plished by Secondary User (SU)-to-Primary User (PU)interference avoidance and mitigation strategies.

• Access to radio environmental information. The secon-dary system deployment should provide the C-MAClayer with radio environmental information by enablinghigh fidelity spectrum sensing mechanisms and/or accessto up-to-date radio environmental information stored indatabases. This radio context information should serve asthe main enabler of radio environmental awareness forthe CRN.

• Advanced spectrum sharing strategies. C-MAC is respon-sible and should provide support for efficient and dynamicspectrum access and resource allocation which aims at in-creasing the overall performance of the secondary systemby exploiting advanced and intelligent spectrum sharingtechniques, that aim to maintain secondary system QoSrequirements via PU-to-SU and SU-to-SU interferenceavoidance strategies. Furthermore, efficient spectrum shar-ing can also serve as a facilitating tool for primary systemprotection.

• Control signaling mechanisms. Fully operational C-MACprotocol requires efficient management and reliable disse-mination of control data through identification, definition,establishment and management of reliable and secure con-trol channel.

More specifically, the C-MAC protocol design addressesplethora of research topics related to the involved functionalrequirements such as: cooperative spectrum sensing, multi-band operation, coordination among network nodes, spectrumaccess and allocation by exploiting advanced artificial intelli-gence and optimization techniques, secondary-to-primary butalso primary-to-secondary interference mitigation and avoid-ance, multi-channel hidden terminal problem resolving, controlchannel (re)configuration, spectrum mobility, etc. The researchcommunity still does not observe extensively several importantMAC aspects (from a legacy MAC point of view) such assecurity, QoS support, scheduling and ARQ procedures.

Most of the proposed C-MAC solutions observe differentsubsets of the main research topics, depending on the ope-rational settings and the targeted application. There is anevident need for classification and systematization of theseproposals to better perceive and understand the advantages


and applicability of the proposed protocols. Traditionally, thedistinction between the different MAC protocols and theirclassification is performed on a basis of the employed mediumaccess scheme [17]. However, due to the complex nature ofthe C-MAC protocols, distinction between them should notbe based on a single aspect such as medium access scheme.The endeavor of classification of the existing C-MAC protocolsrequires definition of general classification framework or layoutthat maps most of the aspects of these protocols in a singleunified representation. Acknowledging many different aspectsof C-MAC protocol, such classification layout should addressthe following requirements:

• Generality. The layout should provide possibility to mapwide range of differing protocols over wide range ofdiverse applications in a single generic and open layoutthat offers unique protocol differentiation and fragmen-tation. In other words, the analyzed protocol should bemodularly mapped over a set of distinguishable functionalblocks with clearly defined interactions that provide firmunderstanding of the protocol behavior and its impact onthe performance of the overall network. The layout shouldalso describe the implications of other factors not directlyrelated to the protocol engineering. In the case of CR,they include spectrum regulations, standardization, etc.Although, the spectrum regulation is not C-MAC issue,it defines the underlying physical medium and imposesgeneral spectrum and resource usage restrictions. As suchit has strong implications on the definition of the com-munication protocol stack, particularly the C-MAC layer,making it an important aspect of C-MAC protocol design.

• Modularity and flexibility. The C-MAC protocol classi-fication layout should be easily extendible in terms ofadopting new concepts and solutions to keep pace with theevolving CR technology and demands.

• Future design enabling/facilitating layout. The layoutshould serve as underlying set of clearly defined andsystematized concepts that can be used as standalonesolutions or combined in advanced and modular C-MACprotocols.

The existing literature does not provide such classificationlayout, even though there are some attempts towards developinggeneric classification criteria. However, they fail to adequatelyand fully describe the inherent multidimensionality of theC-MAC protocols and rarely observe or refer to the impact ofregulatory or standardization aspects on C-MAC protocol engi-neering. In other words, none of the existing surveys addressesall the aforesaid generic classification layout requirements(refer to Section II for details). Therefore, the contribution ofthis survey is threefold:

• It introduces and develops the C-MAC cycle, which servesas generic, open, modular and easily extensible mappinglayout for classification and systematization of C-MACprotocols. The C-MAC cycle is centered on three genericfunctionalities that each C-MAC protocol is required tosupport and offers the possibility to differentiate betweendifferent approaches in addressing the mandatory func-tionalities. Using this approach, each C-MAC protocol,

regardless of its purpose, operational setting and im-plementation details can be modularly mapped onto theC-MAC cycle;

• It offers extensive and general overview on the state-of-the-art advances in C-MAC protocol engineering byreviewing latest technical solutions and proposals in thecontext of the C-MAC generic functionalities providingextensive up-to-date reference list. In particular, it iden-tifies their basic characteristics and places them into theC-MAC cycle;

• It offers brief overview of the latest standardization andregulatory efforts to enable practical deployment of CRNsand highlights how they address various different chal-lenges in C-MAC protocol engineering.

The rest of the survey is organized as follows. Section II re-views the existing work on C-MAC protocol classification anddiscusses their drawbacks. Section III introduces the C-MACcycle classification layout and discusses its capabilities andpotentials. Section IV overviews and recognizes the underlyingideas and technical solutions related to the generic spectrumsensing functionality as the main technical enabler of the radioenvironmental awareness, identifies its functionality-specificaspects and provides an extensive insight on the applicabilityof various techniques for improving the spectrum sensing con-cerning the operational settings. Similarly, Section V gives abroad review of state-of-the-art spectrum sharing mechanisms.Section VI deals with various strategies for establishment andmanagement of control channels in CRNs and emphasizes theimportance of these strategies for the performance of C-MACprotocols and the secondary system in general. Section VIIgives brief overview of latest standardization efforts and reg-ulatory aspects emphasizing their implications on the C-MAC.Finally, Section VIII provides the concluding remarks of thesurvey.

II. RELATED WORK

There are several attempts to develop general criteria forclassification of existing C-MAC protocols and C-MAC re-lated topics. The work presented in [18]–[23] mainly con-centrates on specific CR and opportunistic spectrum accessand particular topics such as control channel establishment,spectrum management policies, multi-band opportunistic com-munications etc. These surveys elaborate on the importance ofC-MAC in CR networking and provide overview of its relevantfunctionalities. However, they do not provide overall C-MACprotocol overview and classification. To the best of the au-thors’ knowledge there are only few papers [24]–[27] that aimto provide survey of general C-MAC protocol classification.This section overviews and summarizes the work presented in[24]–[27] by identifying the introduced classification ap-proaches and criteria. It highlights the main drawbacks of thesesurveys in terms of providing generic C-MAC classificationlayout, and explains the motivation for introducing the C-MACcycle (Section III).

Proposed classifications in [24]–[27] reflect the state-of-the-art research and standardization achievements in CR network-ing, differentiate MAC protocols designed for CRNs from


TABLE IIPROPOSED C-MAC CLASSIFICATION CRITERIA

MAC protocols designed for legacy wireless networks andattempt to identify general C-MAC classification and systemati-zation criteria. In particular, the C-MAC protocols are classifiedwith respect to network architecture (centralized/distributed);applied spectrum sensing technique (local/cooperative); spec-trum access mode (contention-based/time-slotted); spectrumsharing (overlay/underlay/interweave); radio front end config-uration (single/multiple radios); control channel establishment(dedicated/non-dedicated) etc. Table II summarizes the intro-duced C-MAC classification criteria.

The authors in [24] adopt the architecture-based C-MACclassification approach. It is frequent and widely used criterion(especially in legacy wireless systems) that provides systematicoverview of the C-MAC protocols depending on the archi-tectural settings. Under this criterion, C-MAC protocols areusually categorized in MAC protocols for centralized CRNsand MAC protocols for distributed CRNs. The authors providecentralized C-MAC categorization based on whether the centralnetwork controller participates in data transmission (e.g. BaseStation; emphasizing its role in the IEEE 802.22 standard) orjust controls and coordinates the access by temporarily leasingthe spectrum to various secondary systems (e.g. SpectrumBrokers). Furthermore, they review MAC protocols orientedtowards distributed CRNs by identifying the control channelmanagement as the main bottleneck in distributed environmentsand provide distributed C-MAC protocol categorization basedon various schemes for control channel establishment.

The authors in [25] categorize C-MAC protocols into randomaccess, time slotted and hybrid protocols based on the employedspectrum access technique and reviews MAC protocols forcentralized and distributed CRNs. This is the typical approachthat is used for classifying MAC protocols for legacy wire-less systems [17]. However, this approach is not adequate forCR-based networks since it ignores other CR-specific aspects.

One of the most recent surveys [26] proposes different per-spective on CRNs by focusing on optimization and learningmechanisms and divides the existing C-MAC strategies into twobroad classes. The first class comprises C-MAC protocols thatemploy artificial intelligence-based optimization for achieving

global purpose on a system level (e.g. optimal resource alloca-tion and sharing strategies that meet some predefined goals).The second class consists of C-MAC protocols that performsimple negotiation per communication link optimizing someindividual goals. This paper provides state-of-the-art overviewof the latest advances in incorporating artificial intelligence,machine learning and advanced optimization in the CR concept.However, it does not give a clear and broad overview on thelatest proposals and solutions to other crucial C-MAC func-tionalities such as spectrum sensing, control channel manage-ment etc. Moreover, it does not review additional CR suitableoptimization techniques such as simulated annealing, neuralnetworks etc.

Another recent survey presented in [27] mainly focuses ondistributed CRNs and provides number of classification criteriasuch as spectrum sensing, sharing, access mode, number ofradios per node etc. However, some of the classification cri-teria such as the spectrum sharing-based classification shouldbe revised to incorporate up-to-date knowledge acquired re-cently [15]. Regarding the spectrum sharing, [27] classifies theC-MAC protocols as overlay and underlay protocols. OverlayC-MAC protocols refer to scenarios where the SUs are allowedto access only PU-free channels. Oppositely, underlay C-MACprotocols operate in scenarios in which the SUs, subjected topredefined power mask are allowed to access the PU-occupiedspectrum. Recent advances [15], distinguish between threetypes of spectrum sharing: interweave, overlay and underlay.In interweave scenarios, the SUs are allowed to access thePU-free channels. In overlay scenarios, the secondary systemcooperates with the primary system and offers potential benefitsfor the primary system. Finally, in underlay scenarios, the SUsare allowed to use the primary system spectrum as long asthe secondary-to-primary interference is not harmful. Thesesurveys [24]–[27] address common aspects of the C-MAC layerby incorporating results from the latest research and cover,analyze and classify various features of a general C-MACprotocol. However, under closer investigation there are severalmajor drawbacks and limitations in these classification propos-als. In particular, they generally focus on and tend to cover


Fig. 2. C-MAC cycle.

only some specific set of C-MAC features. For example, [26]strongly emphasizes the optimization aspects of C-MAC andclassifies the existing C-MAC protocols depending on whetherthey employ some global optimization mechanism. Focusingon a subset of C-MAC protocol features and rendering all theexisting work on the topic through them, while partially orcompletely circumventing and ignoring other equally importantaspects, results in loss of generality and creation of confusingsemantics. The existing surveys are rich in taxonomy and detail,but poor in generality. They do not provide systematizationand classification layout with unified presentation of all generic(fundamental) and optional functionalities supported by effi-cient and reliable C-MAC protocols. Additionally, the referredsurveys rarely tackle the regulatory and standardization aspectsof CR-based technologies and how they affect the communica-tion protocol engineering and in particular the C-MAC layer.The following survey serves as an attempt towards alleviatingthis deficiency.

III. C-MAC CYCLE

This paper introduces simple, flexible and easily extendibleC-MAC protocol classification and systematization layout re-ferred as C-MAC cycle (Fig. 2), which can easily andmodularly map any C-MAC protocol and its related featuresregardless of the operational scenario and settings. The mainunderlying idea that generates the concept of C-MAC cycleis the recognition that a single C-MAC protocol should sup-port and implement at least three generic functionalities forefficient operation in spectrally heterogeneous environment.

The generic functionalities are presented in Fig. 2 and theyare: radio environment data acquisition (either via spectrumsensing or environmental database), the spectrum sharing andthe control channel management. These functionalities arethe most important CR-related aspects and as such they arewidely regarded as crucial for addressing the C-MAC pro-tocol functional requirements (Fig. 1). The requirement fortheir mandatory support distinguishes C-MAC protocols fromthe common legacy MAC protocols (refer to Table I). Eachof the three generic functionalities is associated to severalfunctionality specific aspects (i.e. aspects that each of thegeneric functionalities encounters and tends to solve) andcommon features, techniques and mechanisms that might beutilized to address the functionality specific aspects, referredto as common aspects. The distinction between common andfunctionality specific aspects can significantly improve theflexibility and modularity of the C-MAC cycle. As alreadyemphasized, no conceptually similar layout has been reported,although the spectrum sensing, sharing and control channelmanagement functionalities are similarly identified as crucialC-MAC characteristics in recent classification attempts (referto Section II). The C-MAC cycle is particularly flexible sinceit provides C-MAC protocol classification, differentiation andfragmentation regardless of the operational settings. Therefore,the C-MAC cycle suits and easily describes wide range of pro-tocols designed for any operational scenario or environment (re-fer to Section III-C for illustrations). The following subsectionsaim to briefly describe the C-MAC cycle in terms of the threegeneric functionalities along with the functionality specific andcommon aspects.


A. C-MAC Cycle Generic Functionalities

This subsection briefly covers the general characteristics ofgeneric C-MAC functionalities and emphasizes their overallimportance for efficient design of C-MAC protocols. It alsointroduces the relevant functionality specific aspects.

1) Radio Environmental Data Acquisition: The radio envi-ronmental data acquisition functionality is a crucial componentof the C-MAC protocols and the CRNs in general. There aretwo possible ways to obtain the radio environmental informa-tion and knowledge, via spectrum sensing or through access toa radio environmental (spectrum) database. These approachesare usually referred to as sensing-centric or database-centricsolutions for CR networking, respectively.

The spectrum sensing is, in principle, a physical layer func-tionality that is tightly coupled with the MAC layer. It is usuallyperceived as the basic tool for acquiring radio environmentaldata in the sensing-centric CR solutions. The radio environ-mental data provides the cognitive engine with the necessaryinformation for building radio environmental knowledge andawareness. Therefore the data provided by the spectrum sensingfunctionality should be highly accurate i.e. it should providereliable and up-to-date information on the spectrum occupancy,availability and usage. The C-MAC protocol should addressseveral functionality-specific aspects related to the spectrumsensing: the time and the duration of the spectrum sensingprocess, the target set of legacy channels to be investigatedby the spectrum sensing, the observation metrics, the spectrumsensing techniques etc. All of these issues, as well as theproposed solutions are addressed in details in Section IV.

A number of C-MAC protocol proposals and CR solutions ingeneral implement the database approach as a mechanism forradio environmental data acquisition. In these database-centricapproaches the radio environmental data is acquired from cen-tral spectrum databases. The spectrum sensing functionality canbe excluded and the C-MAC protocol should provide mecha-nisms to access and retrieve the necessary radio environmentaldata from the spectrum database. In such cases, the existenceof a control channel mechanism to access and obtain spectrumdata from the central database might completely fulfill the radioenvironmental awareness requirement for the operation of theCR nodes.

Various technical solutions for CR networking propose com-bined approach by enabling both the database and the spec-trum sensing solution to improve the overall secondary systemperformance. The concept of Radio Environmental Maps(REMs) emerged [14] recently. REM is a technology enablerthat combines the spectrum sensing-enabled systems with thedatabase approach into a novel flexible and modular conceptreferred to as REM backend technology. The REM backendshould provide variety of mechanisms and algorithms for col-lecting, processing, storing and retrieving different types ofradio environmental data and field measurements. Obviously,the spectrum sensing is not a mandatory C-MAC functionalitybut its implementation can be very beneficial for improvingthe secondary system performance in general. Enabling thespectrum sensing functionality is a challenging task that attractsattention by both, industry and research community. Therefore,

to maintain the generality of the proposed layout, this paperextensively covers the spectrum sensing functionality of theC-MAC cycle.

2) Spectrum Sharing: The spectrum sharing (Fig. 2) isanother generic functionality of the C-MAC cycle. Based on theinput from the radio environment obtained via spectrum sensingand/or spectrum database access, the spectrum sharing strivesto improve the spectrum utilization efficiency via improvedand advanced mechanisms for radio resource allocation andmanagement among the SUs, i.e. the cognitive radios, assuringthe PU transparency and protection. Regarding the functionalityspecific aspects, the spectrum sharing coordinates and orches-trates the medium access, the allocation and sharing of spec-trum resources between various network nodes, addresses themultiple access issues, aims to satisfy the PUs QoS guaranties,etc. Section V extensively addresses all of these issues alongwith proposed solutions regarding the spectrum sharing.

3) Control Channel Management: The existence of the con-trol channel is important for both sensing-centric and database-centric approaches for CR networking. The control channelmanagement is an important and inevitable functionality inthe proposed C-MAC cycle. It should provide mechanism forcoordination, cooperation and collaboration between the CRNentities and the spectrum sensing and sharing processes. Thecontrol channel management deals with different set of prob-lems, such as allocation, establishment and monitoring of a se-cure, available and reliable channel for dissemination of controlinformation (e.g. the outcome of the spectrum sensing, spec-trum sharing decisions and allocations, negotiations betweenthe network entities, dissemination of data for environmentalawareness) in a spectrally heterogeneous environment.

B. Common Aspects in the C-MAC Cycle

The common aspects may refer to the operational modessupported by the CRN that impact the C-MAC protocols def-inition. The number of radios per node is of particular impor-tance. If the CRN supports multiple radio operation, the datapackets can be transmitted concurrently with the control datai.e. spectrum sensing, sharing and control channel managementcan be performed in parallel with the user data transmission.However, in single radio scenarios, the transmission of user andcontrol data is time multiplexed i.e. the CRN operates in split-phase mode, which significantly limits the C-MAC protocoldesign. Additional common aspects of the C-MAC represent-ing the operational mode of the CRN are single band vs.multi-band operation, synchronous vs. asynchronous operation,single antenna vs. multiple antennas, network node mobilityetc. All these aspects impact the C-MAC protocol and highlyaffect how it addresses and deals with the challenges imposedby the spectrum sensing, spectrum sharing and control channelmanagement functionalities.

The common aspects also include a set of techniques,solutions and principles that can be adopted, implemented andutilized for addressing and resolving the functionality-specificchallenges. The reliability of the outcome of the spectrumsensing functionality can be improved through cooperation,coordination and collaboration. Additionally, advanced


Fig. 3. C-Mac cycle for: (a) Centralized CRNs; (b) distributed CRNs.

optimization and learning techniques can be employed todetermine optimal values for various sensing parameters.Similarly, the spectrum sharing functionality may also employcooperation, learning and advanced optimization concepts(e.g. game theoretical algorithms) to determine optimalvacant spectrum utilization strategy based on the operationalmodes supported by the network. Finally, the control channelmanagement functionality may require adaptable solutionsprovided by coordination, optimization and learning to addressspecific issues like control channel availability, saturation andcoverage. The extent, to which these common techniques canbe utilized to address the functionality specific aspects in asingle C-MAC protocol, depends on the operational scenario,i.e. system architecture, application layer requirements, energyefficiency requirements, hardware constraints, the operationalmodes supported by the network etc.

C. Examples of Mapping C-MAC Protocols Into the C-MACCycle Layout

Before dwelling into more detailed description of the genericC-MAC functionalities and their functionality specific aspects,this subsection provides examples (or illustrations) on C-MACcycle’s protocol presentation. The motivation is to illustratehow different C-MAC protocols designed for different opera-tional settings and scenarios can be uniquely fragmented andmapped using the C-MAC cycle in a unified manner. Therefore,this subsection illustrates the mapping of the general class ofC-MAC protocols designed for centralized CRN architecturesand for distributed, uncoordinated CRN over the C-MAC cycle.The classification of C-MAC protocols for centralized and dis-tributed architectures is introduced in [22] and this subsectionwraps up the contents therein by extracting the general aspectsand mapping each protocol class onto the C-MAC cycle. Itshows that the C-MAC cycle appropriately and easily reflectsany C-MAC classification criteria (Table I) proving its general-

ity. Finally, this subsection concludes by showing how to mapa set of specific techniques used in rendezvous based protocolfor control channel establishment (introduced in Section VI-B)onto the C-MAC cycle. It illustrates the mapping of particularfunctionality-specific and common aspects related to protocolsaddressing a specific generic functionality.

1) Distributed vs. Centralized Protocols: The basic func-tional structure of the C-MAC cycle for centralized and dis-tributed CRNs is shown in Fig. 3(a) and (b), respectively. Bothtypes of CRN deployments support the three generic C-MACfunctionalities, namely radio environmental data acquisition,sharing and control channel management. However, the treat-ment and the resolution of the functionality-specific challengesare different in both scenarios. For centralized CRNs the coordi-nation, collaboration and synchronization as common C-MACaspects are strongly emphasized. The sharing and allocation ofspectrum and other communication resources is performed inlogically centralized network entity (usually a Base Station).The radio environmental data acquisition is usually performedusing environmental database access which may be combinedwith spectrum sensing for network performance improvement.Thus, the REM backend solution can be easily employed incentralized CRNs due to global, network wide coordinationand synchronicity. The operation with dedicated control chan-nel (possibly through global or local establishments) is pre-ferred in centralized architectures due to its simple manage-ment and low access and transmission delays in synchronousenvironments.

In distributed CRNs as uncoordinated and asynchronousenvironments, the node mobility, hardware constraints, energyefficiency are of high importance especially in emerging ad-hocdeployments. Node cooperation may be required for improvedCRN performances. The radio environmental data acquisitionis mainly performed through spectrum sensing and the envi-ronmental database approach is not appropriate especially inhighly mobile networks. The spectrum sharing functionality is


Fig. 4. Control channel management based on rendezvous.

performed in distributed fashion and usually employs vari-ous aspects of intelligent decision making and optimization.The non-dedicated control channel establishments such asrendezvous are more interesting in asynchronous, distributedCRNs due to lack of node coordination.

2) Rendezvous-Based Protocols: To attain complete under-stating of the operation and its limitations, each C-MACprotocol must be modularly fragmented into its generic func-tionalities and the set of techniques used to address each ofthese generic functionalities has to be identified. As an exam-ple, Fig. 4 depicts the general mapping of C-MAC protocolswith control channel management based on rendezvous. Therendezvous-based control channel management is appropriatefor distributed, multi-band, multi-user asynchronous environ-ments. The efficient design for hopping sequences that providemultiple overlaps and the average Time-To-Rendezvous (TTR)are the main functionality-specific challenges, which can beefficiently addressed using cooperation between the nodes.

IV. SPECTRUM SENSING

The spectrum sensing functionality of the C-MAC cycleis usually perceived as the most important tool of the CRNfor obtaining environmental data and providing the possiblesolution space for the operation of the CRN in the primarysystem bands. As previously mentioned, depending on theoperational mode of the CRN, i.e. database-centric [28]–[30] orsensing-centric [30], [31] the spectrum sensing may or may notbe covered by the C-MAC protocol, respectively. The databasecentric approaches, currently preferred by the standardizationand regulation bodies (Section VII) usually skip the sensingfunctionality, considering that the sensing technology is notmature yet. However, this section focuses on sensing-centric ap-proaches, where the radio context information is collected andacquired via sensing the frequency spectra, for completeness ofthe C-MAC cycle.

The spectrum sensing is tightly related to the spectrumsharing functionality (Fig. 2), operating in on-demand and in

Fig. 5. C-MAC round in spectrum sensing-enabled CRN.

scenario-depended basis. Depending on the employed spec-trum sharing strategy [32], the aim of the spectrum sensingprocess varies and can focus on providing information on idlePU channels for SU operation, calculating allowable emissionmasks and interference levels perceived by PUs, estimatingthe fading conditions of SU-to-SU channels, learning the PUtraffic statistical characteristics to enhance the SU operation,etc. As depicted in Fig. 2 the spectrum sensing also relates tothe control channel management functionality of the C-MACprotocol since the outcome of the spectrum sensing process (i.e.the environmental data) should be disseminated in the CRN viareliable and secure control channel. Therefore, the specific im-plementation of the spectrum sensing and the amount of radioenvironmental data it generates, impacts the establishment andmanagement of the control channel.

In spectrum sensing-enabled C-MAC protocols, the execu-tion of the spectrum sensing and sharing is usually performedin a time-consecutive manner with the control channel servingas intermediary for dissemination of the appropriate controlinformation (Fig. 5). This time-recurring operation of theC-MAC protocol is referred as C-MAC round in the followingtext. The spectrum sensing aims to detect spectrum opportu-nities based on the radio environmental input. The spectrumsensing output is afterward used as an input in the spectrum


sharing functionality, which performs the medium access, theallocation and sharing of spectrum resources based on thedetected spectrum opportunities. The spectrum access/sharingdecisions, as well as the outcome of these decisions, are fedback to the spectrum sensing functionality to improve the sens-ing performances in consequent C-MAC rounds. The controlchannel is used in cooperative environments to coordinate thesensing and the sharing between the involved network entities,as well as to exchange the outcomes of the spectrum sensingand sharing functionalities.

A C-MAC protocol should address several spectrum sensing-specific challenges and issues (refer to Fig. 2). Namely, itshould determine the time and the period of spectrum sensing,the aim of the spectrum sensing in terms of the observationand detection metrics, the target PU bands in the focus onthe observation, the duration of the spectrum sensing phaseetc. Additionally, the performance and fidelity of the spectrumsensing functionality can be significantly improved by pro-viding support for coordination and cooperation (as commonC-MAC aspects) in a multi-band and multi-user environmentand/or by introducing advanced learning mechanisms that ex-ploit past sensing and sharing experience. The existence ofmultiple radios (per cognitive terminal, e.g. sensing radio andcommunication radio [18], [19], [27]) or multiple antennasper SU affects the choice of the adequate spectrum sensingtechnique (e.g. multi-antenna sensing). This also affects theoperational mode, i.e. how the spectrum sensing is intermingledwith the spectrum sharing (e.g. split-phase or concurrent timeoperation).

The following subsections aim to provide review of thestate-of-the-art solutions to the aforesaid issues regarding thespectrum sensing functionality of the C-MAC cycle. Specifi-cally, they provide overview of the main concepts and ideas inthe area of MAC controlled spectrum sensing with providingguidelines for the selection of the most appropriate spectrumsensing strategy with respect to the specific CRN scenario.

A. When to Sense?

The main goal of the spectrum sensing as a part of theC-MAC protocol is the discovery and the constant tracking ofthe spectrum availabilities/opportunities for the operation of thesecondary system i.e. the CRN. With respect to the sensingexecution time there exist two types of spectrum sensing [4],[33]: reactive (on-demand) sensing and proactive (periodic)sensing.

The reactive sensing is usually performed prior to the spec-trum sharing phase and serves to discover spectrum opportu-nities, depending on the employed/targeted spectrum sharingstrategy. Reactive sensing is performed on on-demand basisand can be engaged to increase the sensing reliability on acertain PU channel, or it can be triggered by the dynamism ofthe environment due to PU appearance or change in the radiopropagation characteristics. Additionally, the reactive sensingcan be triggered as a result of SU mobility.

The proactive sensing is performed periodically. Besidesthe main goal of persistently seeking and tracking spectrumopportunities, the results from the periodic sensing can be

Fig. 6. IEEE 802.22 proactive (fast) and reactive (fine) sensing [4].

used to estimate PU activity patterns, derive the appropriatestatistical propagation models and calculate probabilities of PUchannel availability/occupancy.The proactive sensing is used toexplore for better communication opportunities other than theused one.

The IEEE 802.22 standard [4] defines two types of sensingperiods in the IEEE 802.22 frame: fast sensing periods andfine sensing periods (Fig. 6). The fast sensing periods areused for proactive sensing, with an aim to briefly scan thelicensed TV channels for spectrum opportunities, i.e. whiteholes. Oppositely, the fine sensing periods are used for reactivesensing. The intent of introducing fine sensing periods in IEEE802.22 standard is to improve the detection capabilities of theIEEE 802.22 base stations on a certain frequency band viaextended sensing intervals. This means that the fine sensingperiods can be used to perform more powerful PU signal detec-tion by employing advanced signal detection techniques (e.g.feature detection techniques), which in general require a longersensing period.

In general, the cognitive radio can benefit from the mul-tistage sensing, i.e. the proactive and the reactive sensing.The proactive sensing can be used to constantly track the PUappearance/disappearance in different bands, while the reactivesensing can be used to increase the sensing reliability in certainbands when/where can be necessary or beneficial. However,the overall spectrum sensing time affects the optimality ofthe joint secondary sensing/sharing scheme and there is acertain trade-off between the sensing and the transmission times(Section IV-G).

B. What and How to Detect?

The C-MAC protocol also determines the observation (mea-surement) metric in the focus of the spectrum sensing process.The targeted observation metric determines what should bedetected by the spectrum sensing functionality and its defini-tion is very closely related to the employed spectrum shar-ing strategy and cooperation possibilities. As emphasized inSection II, the recent cognitive radio research has identifiedthree possible spectrum sharing strategies: interweave, overlayand underlay [15]. The following paragraphs focus on therespective observation metrics and employed techniques withrespect to the spectrum sharing strategies.

In interweave scenarios [32], where the goal is to exploreand exploit idle PU channels i.e. spectrum holes whenever thePUs are absent (refer to Section V), the detection of spectrum


opportunities can be performed via signal detection and clas-sification. The signal detection techniques reported in theliterature are classified in two broad classes: blind and featuredetection techniques [35]. The blind detection techniques areused to blindly detect the presence or the absence of any typeof signal in the wireless medium without any prior knowledgeon the type and structure of the underlying PU signals. Theenergy detection and the recently arisen Estimated Noise Powerapproach [36], the autocorrelation detection and the Higher-Order-Statistics detection [34] are typical representatives ofthe blind detection class of signal detection techniques. TheHOS detection has recently become a part of the IEEE 802.22standard, due to the increased detection capabilities. The au-thors in [34] have proposed an enhancement of the HOSdetection, using Goodness-of-fit testing on the skewness andkurtosis on the received frequency bins, proved to detect PUsignals with SNR even below −30 dB. The main drawbackof the blind detection methods is their inability to distinguishbetween different types of signals in the wireless channel,which is especially important in scenarios where SUs operatein uncoordinated fashion. The feature detection methods, inaddition to the detection of signal existence, have the abilityto perform signal classification, and hence, distinguish primaryfrom SUs’ signals, as well as distinguish between differenttypes of primary and SU signals. Typical examples of featuredetection techniques are the matched filter and cyclostationaritydetection techniques. Although the feature detection techniquesare more powerful than the blind detection techniques, theyusually require larger observation time, processing and memorycapabilities of the radio devices.

In cooperative environments [35], [37], in the interweavescenarios, individual spectrum observations/decisions can befused into joint PU signal existence decision by the meansof using hard and soft decision fusion techniques. In thecase of soft decision fusion, the individual observation samplesare summed using Equal Gain Combining (EGC), MaximumRatio Combining (MRC) techniques, as well as other moreadvanced weighted based combining techniques [38], beforemaking the decision on the signal presence. In hard deci-sion fusion methods, such as AND, OR, M-out-of-N rules,Chair-Varshney fusion rule [39], individual decisions of thecooperating nodes are cooperatively fused into joint PU de-cisions. The soft decision fusion techniques aim to providesoft combining of the detection results, by means of weightedsummarization or averaging. In general, these soft fusion tech-niques provide better detection capabilities in the secondarysystem operation as a trade-off with a larger control bandwidthrequirement to exchange the sensing results.

In addition to the binary decisions on PU signal existenceor absence in a certain PU channel, the spectrum sensing canprovide additional information that can be beneficial for theoperation of the spectrum sharing functionality, especially inunderlay and overlay secondary spectrum sharing scenarios(refer to Section V). This information includes PUs’ and SUs’signal and interference levels, signal to noise and/or interfer-ence ratios (SNR, SINR, SIR), fading gains and propagationlosses, information on occurred PU or SU collisions, etc. Basedon the historical spectrum occupancy data, the spectrum sens-

ing functionality can aid the estimation of the channel usagepatterns, traffic statistics, ON/OFF channel model probabili-ties, which can be beneficial in all possible spectrum sharingapproaches. The Radio Environment Maps (REMs) [14],[40], as an emerging CR facilitating technology, can be alsointroduced in cooperative scenarios. REMs can be beneficialmainly in underlay and overlay scenarios [32], where estimateson PU transmitters’ locations [41], propagation losses [42],Radio Interference Fields (RIFs) [42] etc., can significantlyaid the spectrum sharing process. The mentioned estimationscan be done via the processing of the spatial spectrum dataacquired by the cooperative CRs. The REMs can be an es-sential asset to the spectrum management of future wirelessnetworks. Using the REM input, the CRNs can achieve globalnetwork optimization. However, they usually require addi-tional network infrastructure components, such as dedicatedsensor network, database to store the REM data, interfaces toexchange measurement information, etc.

The information provided by the spectrum sensing, coordi-nated and managed by the C-MAC protocol, can potentiallyimprove both, the spectrum sensing and sharing, as well as thecross-layer resource management in CRNs. Additionally, thespectrum sensing information can be beneficial for the controlmanagement for faster configuration of reliable and flexiblecontrol channel that copes efficiently with control channelavailability problems (refer to Section VI).

C. Where to Sense?

One of the main spectrum sensing-specific issues resolved bythe C-MAC protocol is the determination of the target primarysystem bands as well as the amount of sensed primary systembandwidth in a single period of time. This aspect clearly affectsboth, the spectrum sharing functionality and the control channelmanagement functionality. Considering the capabilities of theCR hardware and the operational mode of the CRN [32], thespectrum sensing functionality can cover single or multiplePU bands in a single period in time [44]. CRs that supportwideband operation allow simultaneous sensing of multiple PUbands. However, this capability comes with an increased priceof the CR hardware, thus increased overall price required fornetwork deployment. Oppositely, the CRs with narrowbandcapability, which come at a significantly lower price, are limitedin terms of the amount of PU channels coverage at a time.Therefore, the narrowband CRs usually sweep through multiplePU bands to cover a wider frequency range. The latter solutionis more interesting for the research community and the industry,due to the reduced complexity and reduced hardware and de-vices price, which makes them more attractive for commercialdeployments.

Regarding the bands of interest, the spectrum sensing canbe classified as: in-band and out-of-band spectrum sensing[4], [33]. The in-band spectrum sensing intends to detect PUsignals and avoid harmful collisions with the PUs (and possi-bly SUs) on the same channel where secondary data transferconcurrently occurs. The out-of-band sensing deals with thediscovery of new spectrum opportunities for secondary spec-trum access. The out-of-band sensing phase can be performed


Fig. 7. Gilber-Elliot channel model state diagram.

in a cyclic fashion with the data transmission phase (split-phase operation) or in a concurrent manner when there is multi-antenna/radio capability of the CRs. When the CR terminalshave multiple radios, usually a sensing radio and a commu-nication radio, the sensing and sharing functionalities of theC-MAC protocol can be done concurrently. The authors in [43]have recently proposed a concurrent mode of operation of theCR devices, with available multi-antenna system.

Based on the number of potential primary system bands forsecondary usage the spectrum sensing can be focused on asingle PU band or multiple PU bands [45]. In the single bandscenarios, the SUs aim to discover spectrum opportunities in asingle potential PU band. If opportunity arises, the SU accessesthe channel. The multi-band sensing is more popular researchtopic, since the SUs are allowed to explore multiple legacybands (not necessarily in the same time), which offers increasedflexibility and improved efficiency of the secondary spectrumreuse. The multi-band sensing provides options for a broaderPU environment knowledge acquisition. Therefore, most of thesolutions proposed in the literature and addressed in this surveyfocus on the multi-band multi-user environments.

D. How to Model PU Channels Activity?

Engineering MAC protocols for CR networking requirescertain knowledge and modeling of the PUs behavior andactivity on the primary system band. This means that a certainPU channel model needs to be adopted. Based on the assumedmodel, with predefined (known) or estimated model parametersthe researchers and engineers in the area of CR networkingtend to derive the most optimal spectrum sensing, spectrumsharing and control channel management strategies. The mostcommon PU channel model, extensively used in the area ofCR networking is the Gilber-Eliot channel model [46]–[49].This model is especially useful in slotted PU environments.According to this model, the activity on each of the PU legacychannels follows a two state discrete-time Markov process(Fig. 7). The state of the channel i at time j denoted by Si(j) isassumed to be 1, if the channel is unoccupied by the PUs, and0, if the channel is occupied by the PUs. The state transitionprobabilities denoted by P i

ij remain fixed for a certain periodof time (e.g. few time slots). From the teletraffic theory, thestate and transition probabilities depend on the primary systemarrival/departure process. These state (transition) probabilities

can be a-priori known to the secondary system or can beestimated on-the-fly via spectrum sensing. The latter approachis more interesting, since it provides learning capabilities to thesecondary system, making it more applicable to the dynamicand changeable spectrally heterogeneous radio environment.

When the spectrum sensing performs continuous tracking ofthe PU channel state, the Gilber-Eliot channel model results ina Hidden Markov Model (HMM) [46]. In this case the C-MACprotocol employs the HMM framework. Oppositely, when thereare only partial (sporadic) observations of the PU channel,the model results in a Partially Observable Markov DecisionProcess (POMDP) [46]–[48]. In this case, the spectrum sensingand sharing operate under the POMDP framework.

E. How to Sense?

In addition to resolving where to sense, the spectrum sensingfunctionality of the C-MAC protocol determines the number oflegacy PU bands to be approached for secondary access, the setof the PU channels to be sensed and the order in which thesePU channels will be sensed. As emphasized before, the multi-band spectrum sensing is more interesting and challengingsince it provides additional flexibility and efficiency in sec-ondary spectrum reuse especially in spectrally heterogeneousenvironments. Therefore, this subsection mainly elaborates onthe multi-band sensing approach as the most appropriate for CRnetworking, which is usually regulated by sensing policies. Therest of this subsection focuses on general aspects of spectrumsensing policies as advanced tools to improve the efficiency ofthe spectrum sensing process.

The spectrum sensing policies [47], [48], [50]–[53] repre-sent rules and means to resolve the number, the set and theorder of the PU channels to be sensed in the C-MAC spectrumsensing phase. The spectrum sensing policies highly affect theperformances of the CRN in terms of the PU system reliability(protection) and secondary system performances (throughput,delay, etc.). The next subsections address the sensing policiesclassification in terms of the number of target PU bands coveredin a single C-MAC round, the selected set of target channelswith respect to the learning capabilities and the channel sensingorder, respectively.

1) Policy Classification Based on Number of PU Channels:With respect to the number of PU bands covered by thespectrum sensing process in a single C-MAC round, the sensingpolicies can be divided into two broad classes [45]: single- andsequential-channel sensing policies.

The first class refers to single-channel sensing policies,where the SU operating in a multi-band environment probesonly a single PU channel in a single C-MAC round. If theoutcome of the sensing indicates that the channel is free, the SUcan decide to access the channel or sense another one (if busy)in the next round of the operation of the C-MAC protocol. Thesecond class of the sensing policies, relates to the sequentialchannel sensing.

The CRN nodes adopting the sequential-channel sensingpolicies sense multiple PU channels, before making the channelselection and access/sharing decisions. In the case of sequentialsensing policies, the number of legacy channels covered in a


Fig. 8. Sensing-sharing learning cycle in cognitive environments.

single C-MAC round is a crucial parameter [46], [52], [53],[55], [56]. The performance of the sequential sensing dependson the maximum number of channels a CR node can sense ina single C-MAC round. If the CR node is able to employ thefull observation sensing policy i.e. the CR node is capable ofperforming the sensing of the full PU band of interest, the bestpossible secondary system performance can be achieved. Theexecution of the full observation sensing capability is generallydetermined by the SU devices capabilities in terms of supportfor wideband sensing and time/frequency granularity and reso-lution. However, employing this sensing policy in each C-MACround is not energy efficient. Since the PU activity on some ofthe channels may not change for a prolonged period of time,sensing the whole pool of PU channels in each C-MAC roundmay not be justified due to wastage of energy. These issuespinpoint the need for more optimal and more efficient spectrumsensing policy (i.e. green spectrum sensing policy) [55], [57]that trades-off between the secondary system performance andthe energy efficiency of the sensing process. For an example,a sensing policy with an adaptive/flexible number of sensingchannels that varies with the PU traffic load can be a reasonableand viable solution that provides near optimal secondary systemperformance while limiting the wasted energy due to sensing.

In general, there is a trade-off between the device capa-bilities, in terms of wideband coverage and time/frequencyversatility, and the optimal sensing policy that can be employed.In devices with a higher RF versatility, the sequential-channelsensing can provide better performances. In scenarios with lowcost CR realizations, the single channel sensing policies canprove to offer better sensing performance, due to the largerobservation time to examine multiple channels.

2) Policy Classification Based on Learning Capabilities:Learning is a common C-MAC aspect that can be incorporatedin the spectrum sensing policies to improve the performancesof the generic C-MAC functionalities and the overall secondarysystem performances. Considering the historical spectrum oc-cupancy data and the accumulated experience from previoussensing and access decisions (Fig. 8) can significantly improvethe efficiency of the spectrum sensing. In particular, the histori-cal spectrum sensing data can be utilized to learn the PU trafficmodel and estimate the parameters of the PU channel activitymodel such as channel states probabilities, state transition prob-abilities, belief vectors and predictions of PU channels beingfree or busy in the next time periods etc. The experience of pastspectrum access decisions also has the potential to improve theefficiency of the spectrum sensing functionality of the C-MACprotocol. Negative spectrum access outcomes, such as inabilityto establish SU communication or collision occurrence with theprimary or SUs, can be used to marginalize certain PU channels[54]. The spectrum sensing, spectrum sharing and consequently

the control channel management functionalities will frequentlyavoid these channels. Successful secondary spectrum accesscan be used as an indication to prioritize the underlying PUchannel in the consecutive decisions for sensing and channelselection.

With respect to the learning capabilities, the sensing poli-cies are classified as non-learning [50], [51] and learningpolicies [46]–[48]. By using non-learning policies the CRnode aims to perform the selection of sensing channels with-out considering historical data, such as previous availabilitiesand opportunities in the pool of legacy channels, as well asthe previous outcomes (feedback) from the spectrum sharingfunctionality. Representatives of such non-learning policies arethe random sensing policies [50], [51] and negotiation basedsensing policies [50]. In the case of random sensing policies,each of the contenders for secondary access randomly choosesa channel (single channel sensing policies) or a set of channels(sequential channel sensing policies) from the pool of legacyPU channels without coordinating or cooperating with the otherSUs aiming for spectrum access. The negotiation based sensingpolicies, on the other hand, tend to distribute sensing channelsper SU via negotiation i.e. cooperation between SUs. In thiscase, the SUs exchange the information on the selected chan-nel(s). This information is used by the SUs to select distinctand non-overlapping channels in the next C-MAC round. Itis intuitive that the negotiation based sensing policies, canpotentially result in better secondary system performance due tothe broader primary system knowledge and reduced collisions/interference between the contending SUs. However, these poli-cies require a larger amount of control bandwidth to be ex-changed between the devices, i.e. require more complex CRNrealizations.

The learning based sensing policies [46]–[48] usually adopta predefined primary system model and tend to learn thetraffic parameters of the model, such as state probabilities, statetransition probabilities, belief vectors of PU channels being idleor busy in the next C-MAC round etc. To learn the modelparameters, the learning based policies utilize the historicalsensing data, as well as the previous spectrum sharing feedback.Typical representative examples of the learning based policiesare the myopic sensing [47], [48] and its variations. The myopicsensing performs a constant tracking of the belief vectorsof PU channels being idle or busy in next C-MAC roundsusing a Bayesian inference framework. The sensing channel(s)assignment in the myopic sensing aims to maximize the instan-taneous reward, e.g. the achieved secondary throughput. It isinherent that the combination of learning with cooperation andcoordination among contending SUs [51] can result in opti-mal secondary system behavior achieving a certain degree offairness between the SUs. However, critical aspects that might


Fig. 9. Optimal sensing stopping rule.

seriously degrade the PU and the SU performances can be thePU model mismatch (or misfit), which is especially importantissue in more dynamic indoor propagation environments.

3) Policy Classification Based on Channel Sensing Order:The channel sensing order [45], [49], [53], [58], [59] isanother important aspect covered by the sensing policies. Anefficient channel sensing order can improve the reliability of thecooperative spectrum sensing. In particular, sensing differentchannels in different time instances and on different geograph-ical locations can resolve problems arising from the propaga-tion phenomena, such as the Multi-Channel Hidden Terminal(MCHT) or shadowing related problems. The optimal sensingorder selection among the contending SUs can also yield acollision free secondary spectrum access. Various strategiesand techniques for design of channel sensing orders can befound in the existing literature. For an example, channel sensingorders can be selected randomly from all possible channelpermutations, or they can be selected from a Latin square ofnon-overlapping channel permutations [45]. The choice of theoptimal strategy is usually operational scenario and primarysystem traffic behavior dependent. The sensing order selectionprocess can be aided by learning and other advanced artificialintelligence concepts, efficiently reaching a collision free sens-ing orders based on the feedback from the spectrum sharingfunctionality.

F. How Long to Sense?

The sensing stopping rule and in general, the overall du-ration of the sensing is additional spectrum sensing-specificaspect that requires attention. It is a significant part of a C-MACprotocol that highly affects the optimality of the employedspectrum sensing policies and spectrum sharing strategies interms of primary and secondary system reliability, as well assecondary system performance. There are several proposedsensing stopping rules depending on the strategies and overallgoals adopted by CRN. Namely, the SU can decide to stop thesensing process as soon as it finds an idle PU channel [45]or finds a predefined number of idle channels [52]. The PUchannel sensing process can stop as soon as the estimationof the channel availability exceeds a certainty threshold [51],or in the case of green sensing [60] the sensing process willstop as soon as the consumed energy for sensing exceeds theallowed level.

The optimal sensing stopping rule (Fig. 9) comes from theeconomics [61]. The SU compares the current reward with the

expected reward if the sensing is continued, and stops as soonas the current reward is higher than the expected reward. Theobservation sequence, i.e. the channels’ states, are modeled asrandom variables, x1, x2, x3, . . ., where the observation xi =1 if the channel i is sensed to be free and xi = 0 in theopposite case. The reward sequence y0, y1(x1), y2(x1, x2), . . .,is a function of the channel observations and usually refers tosome secondary system performance metric such as the aggre-gate secondary throughput calculated on the detected availablechannels. In this case, the current reward is the achievablesecondary throughput on the detected PU-free channels and theexpected award is a predicate of what the secondary throughputmight be if the spectrum sensing functionality continues tosearch for PU-free channels. When the maximum number ofsensing channels is K, the sensing stopping problem is afinite horizon problem, where the optimal stopping solutionis found using backward induction method, using the PUchannel state (ON/OFF) probabilities. The parameters of thePU channels’ model can be a-priori known or estimated fromthe historical data. When the secondary system performancemetric of interest is the aggregate secondary throughput, theoptimal sensing stopping rule penalizes longer observationperiods since the reward yn(x1, x2, . . . , xn) is proportionalto the ratio Tcomm/(Tcomm + Tsense), where Tcomm is thecommunication time and Tsense is the overall observation time.

A complexity reduction solution to the optimal stoppingrule can be found in the k-stage look-ahead rule (Fig. 10).Instead of calculating the expected reward for all remainingsensing channels from the pool of K possible channels, thek-stage look-ahead rule compares the current reward withthe expected reward over the subsequent k < K channels.The authors in [61] show that there is only a slight secondarysystem performance decrease in the case of 2-stage look-aheadrule, compared to the optimal stopping rule case.

G. How to Optimize the Sensing?

To improve the spectrum sensing functionality, the C-MACprotocol can employ strategies to increase the sensing per-formance and limit the resource waste. The optimization ofthe sensing, targets the selection of the optimal parametersrelated to the spectrum sensing functionality [49], [62]. Theseoptimization parameters can include both sensing and commu-nication periods’ durations, probabilities of (mis)detection andfalse alarm, etc. All of these parameters are tightly related tothe hardware capabilities and constraints of the SU devices.


Fig. 10. k-stage look ahead stopping rule.

A common optimization goal of the spectrum sensing func-tionality, as a subject of C-MAC optimization, is to max-imize the discovery of spectrum opportunities, and hence,maximize the spectrum efficiency through optimal resourceallocation/sharing strategy [63]. The duration of the sensingperiod is tightly related to the reliability of the sensing, e.g. theprobability of detection of a PU signal and the probability offalse alarm. However, the problem of sensing inefficiency ariseswith the increase of the sensing period duration. In other words,there is a firm trade-off between the sensing and transmissionperiods’ durations. Increasing the sensing period duration re-sults in better sensing reliability but also in inefficient use of thetransmission opportunities [64]. Oppositely, the increase of thetransmission period duration enlarges the spectrum opportunityusage, but false alarms and miss-detections of PU signalsare more likely to occur. The trade-off is illustrated with thefollowing general formulas (1) and (2):

Tsense ↗⇒{Pd(Tsense) ↗;Pfa(Tsense) ↘Tcomm/(Tcomm + Tsense) ↘

(1)

Tsense ↘⇒{Pd(Tsense) ↘;Pfa(Tsense) ↗Tcomm/(Tcomm + Tsense) ↗

(2)

where Tsense(comm) denotes the sensing (communication)time, Pd(fa) is the probability of detection (false alarm), theratio Tcomm/(Tcomm + Tsense) is the spectrum opportunityusage efficiency and the symbol ↗ (↘) denotes increase(decrease).

In addition to the sensing reliability related parameters (suchas probabilities of detection and false alarm), the optimizationof the transmission and sensing periods durations, should alsoconsider the primary system traffic behavior. The research inthis area has shown that there can be found optimal durationsof the sensing and transmission periods that maximize thesecondary system spectrum efficiency and performances withrespect to the traffic load of the primary system [62]. It isintuitive that, when the traffic load of the primary system islower, the sensing duration should be decreased, to maximizethe secondary utilization of the bands. Oppositely, when thetraffic load of the primary system is higher, there should be alonger observation periods, to limit the sensing errors in termsof missed-detections and false-alarms and more efficiently dis-cover and utilize spectrum holes.

The mentioned sensing optimizations are usually performedusing an assumed primary system model (refer to Section IV-D).Under these assumptions, the optimal sensing and transmission

times can be easily derived for different PU traffic parameters.However, in dynamic systems, where the PU activity cannot bemodeled with a known and specific model, advanced learningtechniques can be adopted to dynamically solve the sensingoptimization and cope with unpredictable behavior.

H. How to Coordinate?

The ability to employ the optimal sensing strategies, in termsof optimal sensing parameters and the most appropriate targetchannel sets, sensing orders and sensing stopping rules, ishighly affected by the existence of a coordination mechanismamong the operating SUs. Coordination in a multi-user cogni-tive environment, as a common aspect of the C-MAC protocol,can help the SUs to jointly optimize the channel search sets,orders and time periods, and hence, provide improved oppor-tunity detection capabilities. Sensing the targeted legacy bandsjointly yields a common and broader knowledge of the primarysystem behavior, which consequently results in better spectrumaccess and sharing decisions.

In multi-user environments, the requirement for synchro-nization between SUs further arises, significantly emphasizingthe need of coordination among the cognitive radios. Thecoordination mechanism provided by the C-MAC protocol canfurther enable the quiet period harmonization [4], [33], whichis an important aspect of the CRN, since it provides the SUswith the ability to isolate the PU emissions from the secondaryemissions. The quiet period is defined as the period when allthe secondary radios (CRs) are silent to detect the potentialpresence of an incumbent signal. In centralized CRNs, basestations or access points perform the synchronization and thequiet period harmonization tasks. As an example, the IEEE802.22 standard for CR operation in TV white spaces definesa mechanism for quiet period harmonization and managementbetween the cognitive base stations (Fig. 11). It is evident thatthere is a quiet (silent) period for each specific TV channel usedby the IEEE 802.22 devices to detect a potential presence of aTV signal. The establishment of coordination and synchroniza-tion among SUs, is far more challenging in distributed cognitiveradio environments.

When CR nodes are not able to coordinate their actions,due to the absence of coordination mechanism in the C-MACprotocol then, the nodes operate in autonomous fashion. In thiscase several issues arise. Namely, in autonomous secondarysystems, there is an apparent need of signal classificationcapabilities (e.g. by feature detection) to distinguish primary


Fig. 11. Quiet period management in IEEE 802.22 standard [4], [33].

from secondary signals. Collision avoidance and/or interferencemitigation mechanisms are necessary to alleviate the negativesecondary access/sharing impact on the primary system.

I. How to Cooperate in Sensing?

Cooperation facilitates the spectrum sensing process in termsof increased detection capabilities of the secondary system(higher detection and lower false alarm probability) and in-creased detection agility (lower sensing time when consideringthe cases of wideband or multi-band operation of the CRs).

Cooperative Spectrum Sensing (CSS) allows different SUsto share their sensing outcome by inherent multi-user spatialdiversity [65] to improve the detection performance at theexpense of increased latency and communication overhead. Inthis manner the C-MAC optimizes the CSS strategy by dealingwith these limitations and by taking into account possiblesensing errors. The essential C-MAC optimizations regardingthe CSS strategy consider the aspects of node selection andnode clustering.

The node selection approach [66] exploits the informationregarding the individual characteristics of every SU devicein terms of the spectrum sensing (receiver sensitivity, noiseuncertainty, received SNR, etc.) and communication (minimalrequired SINR) capabilities as well as their relative position toeach other (spatial correlation of the SUs). The node selectionprocess is a complex optimization task which incorporates theabove mentioned node characteristics. In the literature thereexist a number of proposed solutions, which address the prob-lem of node selection by striving to optimize the detectionperformance or secondary system throughput in dependence ofthe number of cooperating nodes, generated control overhead(reporting delay) or consumed energy. Most of the proposedsolutions can be transformed into a convex optimization prob-lem, which is easily solvable [70]–[74]. However, some ofthe solutions result into non-convex optimization problems andrequire advanced techniques and algorithms for optimizationlike particle swarm optimization [66], [75]. The most optimalstrategy choose the lowest number of (non/slightly) correlatedcooperating nodes to comply with the PU protection require-ments, and hence, reduce the latency and the control trafficexchange.

The node clustering technique [67] has been recently pro-posed in the area of CR networking to mitigate the difficultiesexhibited by implementing CSS. Number of research effortsfocus on exploiting the clustering methods to improve the CSSperformance in terms of decreased control channel latency andincreased detection probability [67], [76]–[80]. The node clus-tering process can be divided in three generic methods: randomclustering, where no information regarding the location of theSU and PU is available and the SUs are divided into a givennumber of clusters on a random basis; reference based clus-tering where the SUs are clustered according to their positionswith respect to a given reference and statistical clustering wherethe clusters are formed based on the SUs relative proximities.The statistical clustering proves to offer best PU protection/SUcommunication performances.

The above generic clustering methods prove to be inefficientin terms of the CR energy consumption and achievable sec-ondary system throughput. They require additional green opti-mization solutions regarding the cluster’s features (i.e. numberof clusters, number of nodes per clusters, clustering process,etc.) to minimize the energy consumption [78], [79]. More-over, recent works show that there exits an optimal number ofclusters i.e. number of nodes per cluster that can maximize thesystem performance regarding the achievable secondary systemthroughput.

The learning can be beneficial in cooperative sensing sce-narios. As emphasized by the authors in [81], the learning,i.e. machine learning in particular can be utilized to derive thedetection (classification) regions of the soft decision combiningcooperative sensing. The work in [81] proposes unsupervised(K-means and Gaussian Mixture Models) and supervised (Sup-port Vector Machine and weighted K-nearest-neighbor) learn-ing techniques for detection (classification) of primary signalsin cognitive environments. Using these learning techniques, theCRs will be able to adapt to different SU/PU topologies, and al-leviate problems originating from hidden nodes, or propagationphenomena, such as shadowing or multipath fading.

J. How to Learn?

As already discussed regarding the spectrum sensing policies(refer to Section IV-E), the learning process can improve the


TABLE IIIPOWER CONSUMPTION OF A RADIO [57]

spectrum access and sharing decisions by taking into accountthe sensing history, and vice versa, enhance the sensing de-cisions (channel sets, orders, parameters) taking into accountthe outcomes of the spectrum access and sharing phases. Inlearning aided multi-band spectrum sensing scenarios, a com-mon trade-off that arises is the exploration-exploitation trade-off [46], [49], [56], [68], [69]. Namely, the SUs might decideto probe and access already proven most reliable channels,or it can try to explore new arisen opportunities, e.g. newlydetected unoccupied PU channels. This trade-off highly af-fects the secondary system performances in the cognitive radioenvironment.

In a single-user multi-band scenario, the exploration-exploitation trade-off results in a multi-armed bandit problem,where under the Gilber-Eliot channel model, a Whittle indexpolicy [46] is proven to provide near-optimal secondary sys-tem behavior. In multi-user multi-band secondary systems, thelearning strategy should also consider the joint PU systemstate tracking, in terms of channel state probabilities or statetransition probabilities. The feedback from the access/sharingphases, such as collision occurrences or successful secondaryusage, can assist the learning of the best strategy for spectrumsensing and consequently access and sharing. In the optimiza-tion function the researchers usually provide an exploitationpart, which prefers historically most available channels and anexploration part, which in the form of regret penalizes the mostoften selected channels [69].

K. How to Save Energy?

In addition to spectrum scarcity, energy consumption is alsobecoming a key concern. The spectrum sensing process wastesa considerable amount of energy as well. A sensible energyefficient sensing strategy would enforce the cognitive radioterminal to enter a doze state (Table III) [57] when no data isavailable for transmission, i.e. disable the sensing functionalitywhen it is not necessary. The two stage sensing can also helpsave sensing wasted energy, i.e. the radios can perform fastsensing in shorter periods using energy detection, and performlonger period fine sensing only when it is considered to beessential for the secondary system.

A reasonable sensing/access strategy that can further con-serve energy would be to consider the channel state informa-tion [54], [55] between the SUs. The most available legacychannels are not always the best solutions for the secondaryusage, since they might experience deep fading states andconsiderable losses between the SU pairs. Therefore, the mostsensible strategy would be to consider the state information ofthe wireless channel between the SUs, besides the availability

information. This should result in a joint optimization of thespectrum sensing (to protect the primaries) and the spectrumsharing functionality (to select the channel that can providethe best communication performances, i.e. best secondarySINR) [63].

L. Spectrum Sensing Wrap-Up

The spectrum sensing is an important functionality in theC-MAC cycle and the CRNs in general, providing radio en-vironmental awareness to the CRs, especially in the sensing-centric solutions. The spectrum sensing output defines thesolution space for the spectrum sharing, i.e. the available legacybands as well as the CR communication parameters boundariesfor secondary operation that does not degrade the primarysystem services. The spectrum sensing is also tightly related tothe control channel management, since the sensing outcomesneed to be exchanged reliably among the cognitive networkentities, especially in cooperative CR environments.

This section focused on the spectrum sensing functionalitypresenting the state-of-the-art solutions regarding the sensingfunctionality-specific aspects of the C-MAC cycle (Section III).Specifically, the section provided extensive observation of thekey sensing issues handled by the C-MAC protocol, such asthe determination of time and duration of sensing, the adequatechoice of spectrum sensing techniques and observation metrics,the spectrum sensing policies and highlights how advancedtechniques of learning, optimization, cooperation and coordi-nation can improve the sensing process.

V. SPECTRUM SHARING

The spectrum sharing functionality is the main beneficiaryof the radio environmental information, regarding the C-MACcycle (Fig. 2), and depends on the reliability of the spectrumsensing process. It exploits the given information for efficientsecondary allocation, access, sharing and utilization of thespectrum opportunities. The spectrum sharing functionality istightly related with the control channel management, since allof the required information (from the spectrum sensing process)and its decisions are exchanged via reliable and secure controlchannels (Fig. 5).

The spectrum sharing provides and maintains the requiredQoS to the SUs while striving to avoid harmful interferenceto the primary system. The C-MAC protocol is responsible forenabling the sharing concepts and solutions by coordinating themultiple accesses of the SUs as well as by fostering the processof dynamic (adaptive) spectrum resource allocation. The spec-trum sharing techniques are generally classified as vertical andhorizontal spectrum sharing. The vertical spectrum sharingconsiders the case of opportunistic spectrum access and/orspectrum mobility where the SUs share the same spectrum bandwith the PUs, by exploiting the features of interweave, underlayor overlay spectrum sharing. The horizontal spectrum sharingcontemplates the inter-network and intra-network sharing be-tween the SUs in the CRN. Regarding its actions, the spectrumsharing features can be grouped into several generic processes(or phases) such as spectrum access, channel allocation, power


Fig. 12. Block diagram of the spectrum sharing processes.

allocation (Fig. 12). Additionally, an inherent aspect of thespectrum sharing functionality is the spectrum mobility orspectrum handoff process, which is of crucial importance whenaddressing the primary system protection requirement.

The subsequent subsections cover in extensive details thespectrum sharing processes presented in Fig. 12.

A. How to Access?

The spectrum access represents a complex task in au-tonomous and coordinated cognitive environments, which istightly related to the spectrum decision and channel alloca-tion processes as well as the spectrum sensing functionalityoutcome. It is the key enabler of the vertical and horizontalspectrum sharing and focuses on the aspects of spectrum accessmanagement and multiple access protocols.

The main task in the autonomous spectrum access is thechannel allocation process, which yields information from thespectrum sensing and decision in terms of the available bandsas well as the maximal allowed transmit power of the SUs.In a generic manner the spectrum access is accomplished byachieving individual goals like the QoS requirements [82], [83]or the energy consumption [60], [84]–[86] of a given SU. Interms of the QoS requirements, the approaches presented in[82], [83] focus on achieving/computing the minimal SU trans-mit power that satisfies the individual QoS goals of the SUs.The energy efficiency approaches, are developed to provideincreased energy savings of the SU nodes by optimizing theSU transmit power. For example, the proposed approach in [84]attains the highest energy savings, by computing the optimalSU transmit power based on a Stackelberg game, whereasthe approach in [86], in every transmission slot, selects theSU pair with the highest received SNR to utilize the lowestpossible transmit power. Recent advances have extended theseaspects by jointly addressing both the SU’s QoS requirementsand energy efficiency [87], as well as defining more realisticsystem model assumptions that take into account the imperfectestimation of the SU channels and sensing errors [88]. tomake even more efficient access decisions the spectrum accessprocess can introduce the aspects of learning regarding thesystem’s past decisions and behavior, which ultimately can in-crease the system throughput [89] and achieve autonomous loadbalancing [45].

The coordinated spectrum access is more efficient in termsof the achievable CRN performance but requires the execution

of more complex tasks in the C-MAC protocol. For example,in coordinated scenarios the C-MAC can manage the processof sharing the primary system environmental knowledge, whichcan yield increased spectrum awareness [50], but will inevitablyincrease the implementation complexity. Moreover, in the caseof coordinated access, the C-MAC protocol is envisioned tofoster the load balancing and QoS provisioning and manage thespectrum access process, hence providing an overall improve-ment of the CRN performance [90].

B. How to do Multiple Access?

Multiple access techniques represent a fundamental aspectof the spectrum access process. In CR systems the multipleaccess schemes are based and exploit the features of the legacywireless systems protocols. This section will elaborate themultiple access aspects from a CR point of view.

1) Carrier Sense Multiple Access—CSMA: CSMA is theonly natural “cognitive” (opportunistic) multiple accessscheme. It avoids collisions between the involved radios (SUto PU or among SUs) by adapting the contention windows,back-off durations and access probabilities [51], [91], [92].Moreover, the power control feature can provide increased pri-mary system protection and increased overall secondary systemthroughput. Regardless of the multiple access technique, theoptimization problem boils down to maximizing the SU systemcapacity (or SU throughput) by taking into consideration thecore parameters of the given multiple access technique. In thecase of CSMA the common optimization facet, for secondaryaccess in PU bands, is to maximize the secondary throughputas a function of the sensing time and contention window size,while satisfying the PU protection criteria [91]:

maxτ,W

{R(τ,W )}

s.t. Pd(τ) ≥ Pmind ; 0 < τ < T ; 0 < W < Wmax (3)

where R represents the secondary throughput defined as afunction of the sensing time, τ , and the contention window size,W . Pmin

d denotes the minimal required detection probability,while T and Wmax denote the frame duration and maximalwindow size respectively. In [93] authors have proposed anextension of the common CSMA/CA MAC protocol where theRTS/CTS mechanism is extended to a novel PTS/RTS/CTSone. According to this mechanism, if a given SU has a packetto transmit, it transmits a Prepare-To-Sense (PTS) frame to itsneighboring nodes requesting them to be idle for the followingtransmit period to perform spectrum sensing and PU detection.

2) Orthogonal Frequency Division Multiple Access—OFDMA: OFDMA is one of the most spectrally efficientmultiple access schemes, and can be used to manage thesharing between the secondary and PUs or/and among the SUs.The possibility to adaptively alter the waveform and channelbandwidth makes OFDMA highly suitable for CR use cases. Inparticular, the OFDMA multiple access scheme is very suitablefor interweave scenarios. Moreover, adaptations of OFDM,like Non Continuous (NC) OFDMA can enable aggregation ofmultiple non-continuous available PU bands.


Efficient power management in OFDMA can additionallyincrease the overall secondary system throughput and the pro-tection to the primary system [94]. However, classical schemes(like uniform power loading and waterfilling) are suboptimalfor such interference limited scenarios, as they do not haveconstraints on the radiated interference to the primary system.In the case of CR the OFDMA power allocation focuses onmaximizing the secondary throughput (i.e. transmission rate)while taking into consideration the overall interference imposedto the primary system [95].

3) Time Division Multiple Access/Frequency Division Multi-ple Access—TDMA/FDMA: The TDMA/FDMA access can beapplied to scenarios where the underlying primary system usesthese technologies for multiple access. The aim in this case is tolearn the PU traffic patterns and fill in the time/frequency trans-mission gaps and unoccupied/unassigned time slots/frequencychannels by the PUs [96]. Similarly to the previous multiple ac-cess strategies, power control can provide an increased primarysystem protection and increased secondary system rate [97].

4) Ultra Wide Band/Code Division Multiple Access—UWB/CDMA: The most frequent approach of the underlayspectrum sharing or multiple access utilizes spread spectrumtechniques, such as UWB or CDMA. These strategies aim toincrease the secondary system performance while satisfying thePU interference temperature constraints [98]–[100]. Thus, inCDMA/UWB based underlay sharing approaches the multipleaccess problem is commonly defined around the aggregate in-terference power imposed to the primary system. More specifi-cally, the common optimization facet for secondary access inPU bands is to maximize the SU aggregate throughput as afunction of the received SUs’ SINR and the interference causedto the PU system:

maxγsi,tp

∑i∈N

si (γpi , t

p)

s.t. 0 < Pi < Pmaxi ; γs

i > γ0; tp ≤ tpth (4)

where si denotes the secondary throughput of the i-th SU as afunction of γs

i , which represents the i-th SU’s receive SINRand tp, the PU interference temperature. The Pi and Pmax

i

parameters denote the i-th SU transmit power and its maximalallowable power respectively. The threshold parameters γ0 andtpth reflect the minimal required SU SINR and PU interferencethreshold respectively. Recent advances in the UWB DSAresearch have showed that the time reversal technique can havesubstantial impact on increasing the SU performance and PUprotection. The idea behind this approach is to use an impulseresponse transmission pre-filter (i.e. pre-coder) based on thetime-reversed version of the channel impulse response [101],[102]. This technique provides increased energy gain of theUWB system, thus reducing the interference imposed to theprimary system.

5) Space Division Multiple Access—SDMA: The aspects ofspatial multiplexing in terms of multi-user and Network MIMOtechnology are promising methods for spectrum sharing andmultiple access protocols in CRNs. These methods are capableof enabling multiple systems to simultaneously utilize the same

spectrum band [103], [104]. The SDMA based techniquesrequire high processing power to enable and provide the spatialmultiplexing feature. In CR environments the process gets evenmore complex due to the underlying system assumptions. Itcommonly targets to maximize the SU throughput as a functionof the SU’s transmit covariance matrix while mitigating theinterference caused to the primary system [105], [106], [107]:

maxS

log2 det(I+HSHH)

s.t. Tr(S) ≤ Pt; Tr(GkSG

Hk

)≤ Γk, k = 1, . . . ,K;S ≥ 0

(5)

where, S denotes the SU’s transmit covariance matrix, whileH denotes the channel between the CRN Base Station (BS)and the SU device and Gk denotes the channel between theCRN BS and the k-th PU device. The parameters Pt and Γk

refer to the maximal transmit power of the CRN BS and themaximal allowable interference temperature to the k-th PUdevice. The value K refers to the number of users in theprimary systems, while S ≥ 0 denotes that S is a positive, semi-definite matrix [108]. The latest advances in this area, expandthe existing system assumptions by additionally taking intoconsideration the interference caused from the primary system,to the secondary, thus providing a more generic, robust andrealistic system model [109].

6) Dynamic Frequency Hopping—DFH: The Dynamic Fre-quency Hopping (DFH) scheme is an interweave sharing mul-tiple access technique that hops through the idle incumbentchannels. The IEEE 802.22 standard [110] has adopted theDFH scheme. The rationale behind this scheme is to constantlymonitor for backup channels by means of proactive sensing andthus hop to the backup channel when the PU appears to avoidcollisions or harmful interference [111].

The former part of this subsection elaborated on the specificaspects of the legacy multiple access protocols from a CRpoint of view. Table IV summarizes these aspects and gives ageneric overview of the above mentioned techniques regardinga number of features, such as the type of sharing, multipleaccess dimension, etc.

C. How to Allocate Resources?

The resource allocation process is the fundamental com-ponent of the spectrum sharing which enables and providesefficient utilization of the available spectrum opportunities. Itrepresents an important task related to the C-MAC protocol.The resource allocation is consisted of two generic processes,the channel allocation and the power allocation (Fig. 12),which are commonly managed and optimized in a joint fash-ion. The channel allocation process is responsible for findingthe most suitable frequency and channel bandwidth, whereasthe power allocation process is responsible for managing thetransmit power of the SUs to satisfy the interference constraintsof the primary system. The resource allocation process relatesto two distinct aspects, the resource parameters and the resourceallocation constraints. The resource parameters are tightly re-lated and define the optimization space, i.e. the search space of


TABLE IVMULTIPLE ACCESS SCHEMES COMPARISON

each CR node (i.e. SU) regarding the resource allocation. Theycan focus on several aspects [51], [62], [91], [95], [104], [112]:

• Spectrum related parameters and allocation. The spec-trum related resource allocation utilizes the spectrum in-formation from the sensing process to allocate the mostsuitable carrier frequency and bandwidth to the active SUnodes [51], [62].

• PHY layer parameters and allocation. Commonly thePHY layer resource allocation relates to the modulation,coding, transmission power parameters, for the traditionaltransmission technologies (like OFDM) [95], and can in-corporate additional parameters like antenna configurationwhen considering more advanced transmission technolo-gies like the multi-antenna CR systems [104].

• Upper layer parameters and allocation. The Upper layerresource allocation focuses on the scheduling aspects andbuffer management of the SU system [62], [91] as well ason retransmission techniques like the Automatic Repeat-reQuest (ARQ) procedures [112].

The resource allocation constraints are in general, scenariospecific and depend on the underlying CR use-case. However,they always consist of two generic CR sharing aspects:

• PU protection (Avoiding collisions/interference with theincumbent system).

• SU fairness (Providing a certain QoS level and fairnessamong all active CR nodes).

The resource allocation must introduce the process of opti-mization to exploit the available spectrum opportunities in amost efficient manner. The common optimization problems metin CR based resource allocation scenarios are in general multi-objective [106]. For example, minimizing the packet loss, min-imizing the interference power to the incumbent system as wellas maximizing the throughput are a very likely set of objectivesthat have to be achieved at a single time instance in the resourceallocation process [51], [94], [95], [99], [105]. In [51] theresource allocation is formulated as a multi-objective optimiza-tion that minimizes the interference power to the incumbent

system and maximizes the SU throughput while consideringimperfect spectrum sensing and p-persistent CSMA as the mul-tiple access technique. The authors in [94], [95] elaborate onthe possibilities of ODFM as transmission technology for CRsystems and define the resource allocation process as a multi-objective optimization that strives to maximize the secondarysystem capacity and minimize the interference cause to theprimary system by optimizing the power and subcarrier alloca-tion. In [99], [105] the resource allocation is discussed from theunderlay spectrum sharing point of view. The authors in [99],present the resource allocation process as a multi-objectiveoptimization that focuses on the routing process in ad-hocunderlay/UWB based CR networks, while the authors in [105]elaborate on the multi-antenna (i.e. SDMA) scenario, wherethe resource allocation process is defined as a multi-objectiveoptimization process that exploits the spatial diversity to maxi-mize the SU system performance and diminish its impact on theincumbent system.

The latest advances in the area of resource allocation opti-mization focuses on two distinct methodologies to approach themulti-objective optimization problems, the classical methodsand metaheuristcs based algorithms. Classical optimizationmethods, like single-variable and multi-variable optimization,convert the multi-objective optimization problems in a single-objective problem using a preference-based strategy. Alteringthe preference vector can lead to computing a different trade-offsolution in the subsequent iteration. Classical methods can findonly one solution in one optimization cycle and therefore are in-appropriate for solving multi-objective optimization problems,which usually prefer a set of optimal trade-off solutions [113].In contrast to the classical approaches, the metaheuristcs basedalgorithms utilize a population of solutions in every iteration,instead of focusing on a single one. The capability of thesetypes of algorithms to seek a set of optimal solutions makesthem very suitable for solving the multi-objective optimizationproblem. The most commonly utilized metaheuristcs basedalgorithms in terms of the resource allocation process varyfrom ant colony optimization [119] and Swarm intelligence


algorithms [114], [115] up to Genetic algorithms [116], Dif-ferential evolution [117] and Simulated annealing algorithms[118]. However, the main disadvantage of the metaheuristcsalgorithms is the high computational complexity and lengthyoptimization process. Recent research advances in this fieldstrive to improve these aspects by introducing novel solutions,which commonly exploit the swarm intelligence (more specif-ically ant colony aspects) and provide the possibility for swiftand efficient optimization process [120].

D. How to Cooperate in Sharing?

The aspects of cooperative spectrum sharing are regularlyencountered in distributed environments where the system ar-chitecture lacks a centralized entity coordinating the process ofspectrum access. The cooperative spectrum sharing techniquescan also be classified as horizontal and vertical spectrum shar-ing (which was introduced at the beginning of Section V).

In contrast to the non-cooperative techniques, which aimto optimize the performance of a single CR node (throughtheir local observations and decisions) [50], [60], [82]–[86],the horizontal cooperative spectrum sharing techniques aimto maximize the entire CRN performance by introducing co-operation among all active CR nodes. The horizontal coopera-tive spectrum sharing techniques strive to achieve global (i.e.system level) optimization by solving complex optimizationproblems that commonly focus on the system throughput andthe caused interference [121], [122]. In addition to the through-put and the caused interference recent studies have showedthat the power-bandwidth product metric can be utilized as anpromising and efficient optimization metric for the horizontalcooperative spectrum sharing [123]. The aspect of game theoryis an efficient solution to the horizontal cooperative spectrumsharing problems, because it studies the interaction and cooper-ation among intelligent and rational decision makers, i.e. theSUs in this case. There exist two generic types of coopera-tive spectrum sharing games, bargaining games and coalitiongames, where network users have an agreement on how tofairly and efficiently share the available spectrum resources.The bargaining game is one possible type of cooperative gamein which the entities can reach a mutually beneficial agreement.In this game, the individual players have conflicts of interest;hence no agreement may be imposed on any individual withoutits consent [131], [132]. Recent advances in this area introducethe Nash bargaining approach as an efficient game solution forthe CR resource allocation problems. It’s objective is to performa joint channel and power allocation which maximizes the SUthroughput while taking into consideration the PU protectionrequirements [133], [134]. Coalition games are another type ofcooperative games, which focus on the process of how a setof players can cooperate with others by forming cooperatinggroups and thus improve their gain in the given game [19],[89], [135]–[138]. Commonly the coalition games introduceinterference management (i.e. collision avoidance) mechanism[136]–[138], where each node assigns different weights on thechannels and cooperatively sort their channels [137], to reducethe collisions as much as possible. However, recent researchadvances have shown that the interference management might

be unnecessary for many CR scenarios (e.g. CR vehicular ad-hoc networks) and can be eluded without degrading the overallsystem performance [139].

The vertical cooperative spectrum sharing introduces the no-tion of overlay sharing where the SUs i.e. CR nodes cooperatewith the primary system to achieve the required spectrum reuse.The overlay spectrum sharing denotes to the case where theprimary system alters its transmission strategy to involve thesecondary system and enables the process of vertical spectrumsharing cooperation. The primary and secondary system coop-eration can be established either on the transmitter side or onthe receiver side. The transmit cooperation requires that thesecondary system identifies and knows the primary signal pa-rameters, meaning that it is capable of decoding and processingthe primary transmissions. In the context of an overlay CRNs,whenever the primary transmitter is much closer to the sec-ondary transmitter, the capacity of the PU-SU channel is muchlarger than that of the PU-PU channel to the latter. This allowsthe SU transmitter to obtain the primary message in a segmentof the total transmission time. This channel model is knownas the cognitive radio channel or the interference channel withdegraded message sets [124]–[128]. The additional knowledgegathered at the SU transmitter allows for asymmetric cooper-ation between the PU transmitter and SU transmitter. For anexample, one possible strategy is to have the SU transmitteremploy part of its resources to help the communication betweenthe PUs, so that their communication is not disturbed or iseven improved (e.g. in terms of rates). The remaining SUresources can be used for a dedicated communication to the SUreceiver. Receive cooperation can be an alternative strategy ifthe primary and secondary receivers are close to each other suchthat they can establish efficient communication among them.In the case of receive cooperation, the secondary system canforward the required side information to the primary systemafter decoding its own message. This side information cancontain additional information on the SU’s message, whichthen allows the incumbent system to decode and cancel outthe interference from the SU’s transmission. Another approachis for the SU receiver to forward a compressed version of itsresidual signal after decoding, which can be used by the primaryreceiver to enhance the signal-to-noise-and-interference ratiofor the desired signal component. Receiver cooperation canessentially provide the same benefits as transmit cooperation.However, it requires a modification of the PU’s receiver, toenable cooperation with the secondary system. In general theprimary transmitter should, but does not need to, be ignorantto the cooperation with the CR system. Recent advances inthe field of game theory have shown that the bargaining gamecan be utilized as a technical enabler of the vertical spectrumsharing approach, where the PU and SU systems share thewireless channel as a form of a bargaining process [129], [130].

E. How the Vacate the PU Channel?

Spectrum mobility represents a vital asset of the CR and theC-MAC protocol. It is a direct facilitator of the PU transparencyfeature of the CR nodes. Additionally, it could increase the CRnodes performance by introducing and exploiting the aspects


Fig. 13. Spectrum handover types.

of frequency agility. The spectrum mobility can be realizedthrough four types of spectrum handovers, static, reactive,proactive and hybrid [140] (Fig. 13).

The static spectrum handover is the most inefficient of allfour types. It assumes that CR node will stay on the samechannel and not transmit until the same channel becomes freeagain. The biggest setback of this spectrum handover type is thehigh latency (dwelling latency) incurred by the transmission ofthe primary system.

The reactive type of spectrum handovers is based on vacat-ing the licensed channels after the reappearance of a PU. Theefficiency of the reactive spectrum handovers is tightly relatedto the handover latency. This latency parameter is defined asthe time required for the CR node to discover and switch toa new spectrum opportunity (i.e. channel). In many cases thehandover latency can attain high values and severely degradethe CR system performance, especially in the case of realtime applications, which are commonly defined by tight delayconstraints.

The proactive spectrum handovers utilize some predictivemethods that trigger when the SUs must vacate the under-lying channel. The proactive spectrum handovers can utilizethe features of learning and prediction, where the CRN canlearn the environment dynamics, predict undesirable situationsand act to avoid such situations in timely manner [141]. Asignificant advantage of this type of spectrum handovers isthe achieved low handover latency and minimization of thenumber of future spectrum handovers due to its predictiveand learning features. The drawback of this spectrum han-dover type is that poor prediction and learning can severelydegrade the overall CR system performance. Moreover, theprediction and learning methods require higher processing ca-pabilities of the CR devices due to the increase computationalcomplexity.

The hybrid spectrum handovers represent a compromisebetween the high latency (reactive spectrum handovers) andhigh complexity (proactive spectrum handovers). The Incum-bent Detection Recovery Protocol (IDRP) represents a com-monly used hybrid approach that decreases the handover

latency in the case of reactive spectrum handovers [4]. Itexploits the proactive spectrum sensing (opportunity detection)and reactive decision making. The IDRP approach stores alist of all possible back up channels, which is periodicallyupdated. When an underlying channel needs to be vacatedthe CR node immediately switches to the most appropriatechannel from the IDRP list without having to detect the newopportunities.

One common unrealistic assumption made in the existingworks, (that are addressed in this survey) is that when the SUsare accessing the multichannel system opportunistically, theycan switch to any available channel in the system, regardless ofthe frequency gap between the target and the current channel.However, due to hardware limitations, the SUs can only moveto the new operating frequency, within an acceptable switchingdelay that the devices are typically constrained by. The authorsin [142] study the performance of CR systems, by introducingrealistic channel switching capabilities, where the CR devicescan only switch to neighboring channels without inducing highlatencies.

F. Spectrum Sharing Wrap-Up

Spectrum sharing is a facilitator of the dynamic resource uti-lization. The goal of the spectrum sharing process is to provideand obtain the best possible QoS for the active SUs, whileavoiding harmful interference to the primary system(s) andachieving the highest spectrum reuse. Including some specificsharing procedures such as, spectrum access, resource alloca-tion and spectrum mobility achieve this goal. The mentionedprocedures rely on complex optimization algorithms and aredependent on the reliability and performance of the C-MACprotocol, especially its spectrum sensing and control channelmanagement functionalities (Fig. 2). The control channel man-agement heavily influences the performance of the spectrumsharing process, because all spectrum sharing decisions andactions are distributed through the established control chan-nel(s). The following section elaborates the main features ofthe control channel management.


Fig. 14. Information flow through the control channel.

VI. CONTROL CHANNEL MANAGEMENT

The Control Channel (CC) in CRN differs significantlyfrom the usual legacy wireless communication channel used forexchange and dissemination of control information in existingwireless networks. The CRN operates in spectrally heteroge-neous environment (refer to Section I) i.e. in highly dynamicenvironment with spectrum availability that varies in time,space and frequency. The characteristics of such environmentsarise challenges such as coverage, saturation and security [23].Depending on the target application and operational mode ofthe CRN, the CC management functionality of the C-MACprotocol should decide where and how to establish the CC tomitigate the mentioned problems [148].

The CC provides its services to a number of CRN operationalaspects [170] (such as network self-organization, network co-ordination, synchronization, cooperation, collaboration, spec-trum mobility, flexible data connections and increasing andattaining overall spectrum efficiency) through enabling dis-semination and exchange of reliable control information. Thecontrol information can be classified in several general types(Fig. 14):

• Up-to-date radio environmental information, which can beacquired either through spectrum sensing or by retrievingit from a database, when the existence of CC is crucialfor access to the database. When the spectrum sensingis enabled, beside the sensing outcome, this informationcan also consist of other sensing related parameters (e.g.PU channel parameters). In REM enabled scenarios theCC serves for access and information retrieval from theREM as well as storing sensing information in the REMstorage;

• Spectrum access/sharing and resource allocationdecisions;

• Additional information such as coordination, synchroniza-tion and other scenario dependent information is alsoexchanged through the CC. However the amount of thisinformation heavily depends on the operational scenario,settings and limitations.

Therefore, the importance of the CC in the CRN is significantand the implications of poor CC establishment and managementstrategies are considerable [23], [148], [156], [169]. The fol-lowing subsections cover the CC allocation, establishment andmanagement as its specific aspects along with the commonlyused techniques for efficient addressing of the CRN specific CCchallenges.

A. Where to Establish the Control Channel?

One of the key issues with respect to the CC establishmentand management is whether the channel should be establishedas in-band or out-of-band channel, regarding the licensed pri-mary band in which the CRN operates. The in-band CC [147],[152], [156], [157] is usually established in the bands usedfor data transmissions both as dedicated and non-dedicatedCC. Oppositely, the out-of-band CC [61], [146] establishmentsallocate spectrum bands for control information exchange thatare physically separated from the bands used for data trans-missions (e.g. the guard bands of the licensed band or theunlicensed bands such as the ISM band). Such CC alloca-tions have several advantages in comparison with the in-bandCC establishments. In particular, the out-of-band CC usuallyprovides global network coverage and coordination since it isusually allocated on dedicated out-of-band spectrum resources(in the licensed band in which the CRN operates or in theunlicensed band). Therefore, the out-of-band CC allocation issuitable for centralized network architectures. However, someCRN deployments may find the out-of-band CC impracticalsince it is usually expensive solution that requires tight man-agement (e.g. resolution of the coexistence issues when the CCis established in the unlicensed band, or when multiple CRNsuse the same resources for control data exchange). There-fore, the in-band CC establishments might be appropriate fordistributed, asynchronous and uncoordinated ad hoc networkdeployments due to their flexibility and potential resilience onsaturation and security attacks especially when established asnon-dedicated CC.


Fig. 15. Control Channel establishments.

Fig. 16. Global dedicated control channel establishment.

B. How to Establish the Control Channel

Based on the existing work on secondary system CC man-agement in the target primary band and in the context of theC-MAC cycle in Section III, the classification of commontechnical solutions for CC establishment is shown on Fig. 15.

The existing CC establishment mechanisms can be classifiedin two broad groups: Dedicated CC establishments and Non-Dedicated CC. The Dedicated CC (DCC) solutions refer toCC establishments where a set of spectrum resources (i.e. de-tected PU-free channels) is solely dedicated for the transport ofsecondary system control information (i.e. no SU data packetsare transmitted over these resources). Note that the dedicationis performed by the C-MAC protocol and is transparent to theprimary system i.e. the PUs can access the channels allocatedfor the CC when needed. The other group of technical solutionsfor CC establishment consists of Non-Dedicated CC (NDCC)proposals. In the NDCC enabled CRN, the SUs share the avail-able PU-free channels of the target licensed band for exchangeof both control and data packets. Hybrid approaches that exploitthe advantages of both DCC and NDCC establishments havealso been proposed [151].

1) Dedicated Control Channel: The DCC can be realizedas a Global DCC, Local DCC or Dynamic DCC. When GlobalDCC (GDCC) is established, all SUs in the CRN tune to asingle globally available CC allocated on globally common setof PU-free channels to exchange control messages. An exampleof general GDCC establishment and its usage when PU reap-pears is provided on Fig. 16. The authors in [61], [146], [148],[152] discuss different aspects of the GDCC establishment andoutlined some of its major advantages and drawbacks. In par-

ticular, the GDCC provides high level of network coordinationand global coverage [61]. However, it exhibits the saturationproblem due to the limited channel capacity, i.e. the CC tendsto saturate as the number of SUs increases, being the majorbottleneck in the CRN. Moreover, the CC availability problemimposes large limitations on the network performance since theGDCC does not efficiently address the spectrum heterogeneityproblem [146]. Thus, the PU channels allocated for the CC maybecome temporally unavailable which may cause disruptions ofthe secondary system communication links. Therefore, variousproposals for intelligent choice of the underlying spectrumresources for establishing the GDCC have been reported. Mostof these proposals heavily rely on global optimization (andother advanced techniques such as game theoretic methods)as common aspect of the C-MAC protocol that provides op-timal solution in terms of maximizing the availability and theresilience to PU activity and security attacks of the GDCC.As an example, the authors in [153] propose jamming-resilientcontrol channel game that converges to a Nash equilibrium andsuccessfully combats jamming when the PU is active and inthe presence of sensing errors. Another interesting approach isreported in [146] where the control channel is allocated usingOFDM in the guard band of the primary channel to avoid theavailability problem and to enable uninterrupted transmissionof control data.

Alternative DCC establishment mechanism that alleviatesthe single point of failure effect exhibited by the GDCCand addresses the spectrum heterogeneity more efficiently isthe Local DCC (LDCC), which is similar to the GDCCbut on a local level. When establishing LDCC, a subset of


SUs exchange controls packets via locally common CC i.e.the SUs exchange control information over locally commondedicated PU-free channels. The local decision making uponwhich channels will be used for the LDCC establishment isextensively studied and various frameworks have been pro-posed. The authors in [172] propose various node groupingmechanisms to establish LDCC. The node grouping mecha-nisms represent a set of techniques that do not require thepresence of group coordination node i.e. the decision whichcommon channels to allocate for LDCC is distributed amongthe SUs in a group. Similarly, the work presented in [149], [150]establishes the LDCC by employing various node clusteringmechanisms. Opposite on the node grouping based LDCCestablishments, the node clustering approaches for LDCC es-tablishment imply local coordination and centralized chan-nel allocation for the LDCC. The LDCC establishments viaclustering facilitate the cooperative sensing, routing, topologymanagement, etc. Another interesting approach is presented in[152] where the authors use cloud-based techniques to formthe LDCC. Achieving network-wide inter-group/inter-clustercommunication is of crucial importance with LDCC establish-ments. The availability issues and potential local saturationproblems can be alleviated by quick and efficient re-groupingand re-clustering which represents another extensively studiedresearch topic of LDCC. In general, both GDCC and LDCC areprone to security attacks (e.g. jamming).

Both GDCC and LDCC are usually allocated statically i.e.the same channels are used for long period of time whichdecreases the availability of the CC. By performing CC re-allocation and reconfiguration whenever the underlying PUchannels dedicated for CC establishment become unavailable orchange in the primary system behavior (e.g. increased PU load)is detected, the Global/Local DCC is allocated dynamically.This specifies the Dynamic DCC (DDCC) [145], [149], [150],[163], [166]. If the CC reconfiguration procedure is supportedby a back-up control channel (which may be allocated ineither the licensed or the unlicensed band) then the channelreallocation for CC establishment can be performed during anongoing data communication and it does not require disruptionof any communication link. The CC reconfiguration can beperformed by using learning mechanisms that adapt to and tryto predict the PU activity based on PU channel modeling andthe historical sensing and sharing/access data [145]. Efficientdynamic reconfiguration can potentially circumvent the avail-ability problem exhibited by the Dedicated CC establishments.Including this type of dynamism in the DCC establishment andmanaging, the CC saturation problem is efficiently alleviatedwhile achieving high robustness to PU activity and resilienceto security attacks. Moreover, this type of DCC can adapt tochanges in the SUs traffic demand while providing “alwayson” transparency to them, which significantly improves theperformances of the CRN.

2) Non-Dedicated Control Channel: The NDCC establish-ments alleviate the major disadvantage of the DCC establish-ments since they do not require for a set of channels to bePU-free. Therefore, they circumvent the CC availability prob-lem. In general, the NDCC establishments enjoy high robust-ness to PU activity. The NDCC can be realized via FrequencyHoping, Rendezvous or as Ultra Wideband.

Fig. 17. Non-dedicated control channel realized by frequency hopping.

Fig. 17 shows an example of Frequency Hopping NDCC(FHNDCC) [144], [147], [148], [171]. The network nodeshop across channels i.e. predetermined frequency carriers fol-lowing the same deterministic pattern referred to as hoppinglist. This solution requires tight synchronization between thenetwork nodes, which can be challenging to achieve especiallyin distributed ad-hoc network architectures. The data channelcan be realized either by hopping (i.e. when the nodes fromthe communicating pair hop on a channel from the hopinglist, they exchange both control and data packets, Fig. 17) orit can be negotiated during the control information exchange(i.e. the nodes negotiate the resources to be used by the datachannel by using the FHNDCC establishment). The hoppinglist can be global and unique for whole network, it may havelocal reference or it may be dynamic and adaptable to varyingnetwork conditions (e.g. channels that experience decreasingPU traffic load may be hopped more often than channels expe-riencing increasing PU traffic load which is the basic approachadopted in [147] where the hopping sequence is dynamicallyadapted to the primary user activity pattern). The FHNDCCestablishments with dynamic and adaptable hopping lists arereminiscent to the Dynamic DCC establishments. FHNDCCmay suffer from potentially large transmission delays, sincetuning to a new frequency carrier is performed in each hop.This delay (if large) can impose significant constraints to certainapplications (such as real time applications) in both cases whenthe data channel is realized by hopping or negotiated duringthe hopping.

Rendezvous NDCC (RNDCC) establishment differs fromFHNDCC in the hoping sequences used by the CRN nodes[154], [155], [159]. Under the RNDCC establishments, thenodes use different hopping sequences that overlap at certainpoint. When overlapping occurs, the SUs that want to com-municate exchange the control information. If not properlydimensioned (i.e. if the choice of the hopping lists is poor) thissolution will inevitably suffer from large delays and severe lim-itation of the control channel capacity. However, if the hopinglists are properly designed then the RNDCC establishment isfairly simpler than the FHNDCC since it does not require fornetwork-wide synchronization between the nodes. To supportlarger amounts of control information, the RNDCC implemen-tation requires usage of hopping list that overlap multiple times.


Fig. 18. Non-dedicated control channel realized by asynchronous rendezvous.

In the same time, multiple nodes can exchange control databy using different hopping list. The design of the hoping listrequires the probability of node collision to be made arbitrar-ily small. Similarly to the FHNDCC establishments, the datachannel can be realized by negotiating during the rendezvousphase, or the user data can also be exchanged by rendezvous.Note that hybrid FH/R-NDCC solutions can be devised. Inparticular, after the nodes achieve rendezvous (i.e. they meeton a certain channel), they can continue to hop followingcommon negotiated hoping list in a FHNDCC fashion. Therendezvous may be synchronous or asynchronous [160]. Theasynchronous rendezvous is not vastly studied topic. Both typesof rendezvous require specific considerations when devising theset of hoping lists. The incurred delay can be significantly de-creased by minimizing the Time-to-Rendezvous (TTR), whichcan be achieved by proper choice of the hoping list set. Thedesign of synchronous hoping lists with minimal TTR thatachieve multiple rendezvous is vastly studied topic and manyproposals have been reported. The hopping lists can be chosentotally blind from casual random sequences but without anyguaranties that rendezvous will occur. A common approach indesign of the multiple rendezvous hopping sequences is thetheory of quorum sequences (i.e. sequences that will surelyoverlap at least once) and modifications of this mechanismare targeted in work presented in [159], [160], [162], [164],[165]. Moreover, to achieve multiple overlaps [159], [163]propose to use cyclic quorum techniques. Achieving globalnetwork coverage is a challenge when establishing RNDCC.The coverage of the RNDCC is per communication link, anddedicated hopping lists can achieve global network challenge.Fig. 18 shows asynchronous rendezvous between two nodeswith multiple overlaps.

Another NDCC establishment is via UWB technology [17]i.e. the Ultra-Wideband NDCC (UWBNDCC). This type ofCC establishment is in the focus of [157], [158], [161], [167]where the authors outline the main design and implementationchallenges regarding its establishment. The control informationis spread across large bandwidth (over large part of the dispos-able spectrum, or it may be spread over whole disposable spec-trum) in underlay fashion. This establishment clearly does notsuffer from the usual CRN CC problems like availability sincethe channel is established in underlay mode [143], but dealswith other UWB specific issues. Being UWB technology based

establishment, strict limitations on the transmission power maybe imposed on this type of CC, thus making the transmissionrange limited, and therefore achieving global network coverageis a challenge. This limitation also makes the UWBNDCC verysusceptible to saturation. One of the research challenges for thisCC realization is the increase of the control data transmissioncoverage and rate without causing harmful interference to thePUs. This challenge can be addressed by design of spreadingcodes that achieve optimal trade-off between the coverage andrate. As highlighted in [157], another problem with the UWB-NDCC establishment arises when different radio technologyis used for data communication (rather than UWB, whichis usually the case) transmission range differences appear. Asecondary system node can be one hop neighbor to anothernode and achievable in case of data communication radio (e.g.WLAN), but not necessarily achievable under the UWB radio.This effect requires design of efficient control packets routingprotocol to exchange control information, which results inincreased control overhead. Such routing protocol, designedspecifically for this purpose can be found in [157].

C. Control Channel Management Wrap-Up

The existence of available, reliable and secure CC as genericC-MAC functionality is very important for both, the sensing-centric and the database-centric approaches for CR network-ing. The CC should provide mechanisms for coordination,cooperation and collaboration between various CRN entitiesand the spectrum sensing and sharing functionalities. Table Vsummarizes the discussed characteristics and behaviors of thevarious CC establishments.

The main observations indicate that the DCC establish-ments provide low CC configuration and CC access delaysand provide large, network-wide CC coverage but suffer fromsaturation, availability problems, low PU activity and securityattacks resilience. That is, the GDCC and LDCC establish-ments support high network-wide coordination but they alsorepresent a single point of failure for the secondary system.DDCC introduces dynamism that increases the reliability of theCC at the price of increased implementation complexity. TheNDCC establishments are highly robust to the saturation andavailability problems, resilient to security attacks i.e. they tendto completely eliminate the single point of failure effect, butthey impose challenges on achieving global coverage and incurlarge configuration and, especially, access delays.

The work presented in [148] compares DCC establishments(specifically GDCC) with the NDCC establishments (specif-ically multiple overlaps enabled RNDCC and FHNDCC) bytesting their performance with respect to several metrics usingsystem level simulations that mimics uncoordinated PU trafficpattern agnostic secondary system. As elaborated there, ingeneral, the NDCC establishments perform better in terms ofaverage secondary throughput and average per-packet delay forlow to moderate PU loads. The NDCC establishments in thereferred paper are also shown to exhibit better primary systemprotection properties. Notably, in this work the RNDCC withmultiple channel overlaps outperforms all other CC establish-ment techniques.


TABLE VQUALITATIVE COMPARISON OF VARIOUS CONTROL CHANNEL ESTABLISHMENTS TECHNIQUES

VII. REGULATORY AND STANDARDIZATION EFFORTS

RELATED TO C-MAC PROTOCOL ENGINEERING

After introducing the C-MAC cycle, its functionalities, andtheir common and functionality specific aspects in terms ofstate-of-the art advances in the respective areas, this sectiondwells into the CR regulatory and standardization efforts,and their influence and reflection in the C-MAC protocolengineering.

Over the past few years, there has been a significant progressin the spectrum regulation domain to address the growingdemands of radio communication services. The regulatory andstandardization bodies, have mainly focused on the cognitiveuse of TVWS, as the most advanced cognitive use-case sce-nario, while several (yet still significant) efforts have beenmade in the definition of spectrum markets and (re)farming[173], definition of collective spectrum usage licenses, such asauthorized/licensed shared access (ASA/LSA) [174] etc. Theregulators have organized numerous consultations, collectedopinions and proposals with respect to various CR use-cases,some of which progress towards definition of standards andtechnical solutions, by respective standardization bodies suchas the International Telecommunications Union (ITU), IEEE,ECMA and the European Telecommunications Standards In-stitute (ETSI). The standardization efforts have been mainlyconcentrated around the enhancement of existing networktopologies (such as LTE) with spectrum sharing capabilities,enriching the Heterogeneous Networks (HetNet) concept, in-troducing the Machine-to-Machine (M2M) communication, aswell as the exploitation of TVWS by enhancing existing orcreating new standards. Most of the solutions have focused ondatabase aided CR approaches, since the respective regulatorybodies have identified the sensing functionality of the C-MACas immature, yet still, the spectrum sensing functionality hasbeen envisioned as a potential and efficient tool for the futureto handle the dynamic management of spectrum resources interms of controlling the aggregate interference (to/from theprimary system) and global optimization of spectrum resourceutilization.

This section aims to present the latest developments inseveral cognitive radio use-cases, presenting the ongoing stan-dards and proposals, and their instantiations with respect to the

C-MAC cycle in terms of mandatory (optional) functionalities.The section is organized as follows: Section VII-A focuses onthe standards with respect to the cognitive use of TVWS scenar-ios, while Section VII-B presents the progress in the expansionof the broadband networks with cognitive capabilities. Finally,Section VII-C presents the respective standardization activitiesin the M2M cognitive use-case scenario.

A. Cognitive Use of TVWS

The cognitive use of TVWS scenario has been the most pen-etrated CR use-case, since the first introduction of the cognitiveradio principles. The standardization bodies has initiated thedefinition of two IEEE standards to exploit spectrum holes inthe TV bands, as well as the investigated of other potentialCR applications in the same bands. The IEEE 802.22 WirelessRegional Area Network (WRAN) Standard [4] defines the op-eration of IEEE 802.22 WSD devices, with respect to the usageof TVWS, in terms of proper protection of TV incumbents,compliance with regulatory domain policies, as well as inter-operability among different WRANs. This standard comprisesall three generic functionalities of the C-MAC cycle. Usingthe Spectrum Sensing Automation (SSA) and the SpectrumSensing Function (SSF) components, the IEEE 802.22 com-pliant devices (BS and Customer-Premises Equipment (CPE))can coordinate and execute the spectrum sensing when required(see Section IV-H and Section V-F). The Spectrum Manager(SM) components performs the spectrum sharing functionalityof the C-MAC cycle, obtaining, maintaining and managinginformation on spectrum availability as well as making andenforcing appropriate policy-compliant decisions. The controlchannel is mandatory in the IEEE 802.22 standard, to providethe coordination, cooperation and collaboration between thedevices. Recently, there have been two amendments of thestandard, i.e. the IEEE 802.22a and the IEEE 802.22b amend-ments [5]. The IEEE 802.22a amendment creates standardizedmanagement and control interfaces as well as procedures thatallow for management of multiple cell operation, communi-cation with a TV incumbent database to provide a list ofavailable channels and possibly the maximum EIRP allowableon these channels, and a Geolocation Function, which providesthe necessary information to determine the location of the


IEEE 802.22 devices. The IEEE 802.22b amendment specifiesenhancements to IEEE Std. 802.22-2011 to support enhancedbroadband services and monitoring applications, such as sup-porting new classes of 802.22 devices, large number of low-energy (-complexity) CPEs, different QoS classes for criticalapplications, ad-hoc, point-to-point and relay connections etc.

The IEEE 802.11af standard [6], [175] presents the technicalamendments to enable legacy IEEE 802.11 WLAN system tooperate in the TVWS. In response to the proposed geo-locationTVWS spectrum database from the regulators, the standardspecifies a common framework consisting of the TVWS spec-trum database, the master/slave WS Device (WSD) classes andan optional entity called the Registered Location Secure Server(RLSS). This standard currently uses two of the functionalitiesof the C-MAC cycle, the control channel functionality, neededto communicate with the TVWS database and the optionalspectrum sharing enabling functionality, residing in the RLSS,providing receiver-based information on the operating parame-ters for the WSDs.

In [176] the authors investigated the suitability of TVWSbands for cellular network use. Their main observations are thatthe due to the high interference from TV towers, it is difficult tofind channels that would allow a good performance of cellularnetworks, and that the TVWS is primarily suitable for trafficoffloading on hotspots, or building small cell networks ratherthan for large contiguous coverage networks. The application ofcognitive functionalities into small cells for the use in TVWShas been proposed in some studies. The scope is to enhance thecapacity while avoiding interference to/from the TV service. In[177] the first LTE-TVWS prototype was proposed, to demon-strate the feasibility of dynamically utilizing TVWS spectrum,via real-time REM information. The small-cell scenarios inTVWS, should incorporate all the functionalities of the C-MACcycle, i.e. the spectrum sensing (and/or a REMs) and spec-trum sharing, to coordinate and optimize the usage of TVWS,between the TV incumbents and the small-cells, as well asbetween small-cells. The control channel is a necessity tocoordinate all the actions.

B. Cognitive Evolution of Broadband Networks

Recently, LTE is the dominant state-of-the-art cellular broad-band network standard. The 3GPP LTE releases introduce andcontribute to the Heterogeneous Networks (HetNets) concept,introducing small cells, such as microcells, picocells, relays andfemtocells [178], [179] in the classical macro-cellular network.The gradation of small cells is made based on the coveragearea, i.e. the transmit power of the eNodeBs. The picocells andthe microcells follow the same frequency allocation conceptsas the macrocells, but with lower coverage areas, they arefully controlled by the operations, using the X2 interface forcontrol traffic exchange and the Inter-Cell Interference coordi-nation techniques to manage the inter-cell interference. How-ever, in the case of the femtocells, there is no clear definition ofinterfaces, techniques and solutions to manage the installationand the optimal femto-cell operation. They are intended to oper-ate with low transmit powers in mainly indoor environments, tobe installed by customers and operate in an unplanned manner,

using a DSL connection to the backhaul of the cellular network.Due to the unavailability of the standard ICIC techniques, theresearch community has envisioned a great deal of cognitive po-tential in the femto-cell optimization. In general, these scenar-ios should include all of the functionalities of the C-MAC cycle.Based on the radio environmental input (preferably real-time,either sensing and/or REMs) the femto-cells should employadvanced spectrum sharing techniques to optimize the spectrumusage (and minimize inter-femto or cross-femto-macro interfer-ence) and control information exchange resources.

The multi-tier heterogeneous network can be comprised ofdifferent technologies as standards, as well. The concept ofIntegrated Femto-WiFi (IFW) can be used to offload trafficfrom the LTE macro-network in residential buildings, metrostations, business objects etc. Although it seems natural for theoperators to expand into new spectrum to cover their needs,this cannot be supplied due to the limited amount of availablefrequency bands. In these scenarios, having the REM conceptand a fully functional C-MAC cycle would be a great asset forthe optimal radio (spectrum) resource management.

Another area of innovation addressed by the standardizationbodies is the use of Licensed Share Access (LSA)/AuthorizedShare Access (ASA) [180] for mobile broadband services.These licenses would allow timely exclusive license, i.e. spec-trum reservation for secondary usage in a given location/frequency/time boundaries. In this manner, licensed spectrumuse of mobile services with predictable QoS can be enabled.The LSA/ASA concept has been recently adopted by ETSI[9], proposing a bandwidth expansion of the mobile networkin licensed bands within the 2.3–2.4 GHz frequencies. TheLTE operator can apply for individual authorization of spec-trum usage from the regulator for a specific region and timeperiod, and the regulator can set the boundaries of the LSAbased sharing. Another, spectrum market related concept, isthe spectrum trading [181], where the spectrum can be sold orrented based on the overall terms of the original agreement withthe regulator authority. These secondary spectrum markets canenhance competition and reveal new spectrum opportunities forcompanies. These advanced spectrum licensing and spectrumtrading based approaches, operating in centralized coordinationbetween the operators and the regulators, would circumvent theneed of some of the functionalities of the C-MAC cycle, such asthe sensing (and/or the sharing functionalities), since only thecontrol traffic exchange would completely cover the networkmanagement.

C. Machine-to-Machine Communications

The CR research community has also envisioned theMachine-to-Machine (M2M) communications, as one of thepotential cognitive radio use cases. The M2M communication,including communication between machines, with respect tohealth monitoring, security, remote control, ambient assistedliving, etc., is expected to grow exponentially in the near future.The respective standardization activities in the area of M2Mcommunications can be seen in the 3GPP Technical Report37.868 [183] and the IEEE amendments 802.16p, 802.16.1b[184] and 802.15.4m [185].


Fig. 19. Outline of C-MAC protocol operation.

One of the most critical aspects in the M2M communicationtechnical solutions is the green aspect, since the M2M devicesshould be low-cost and low-power consuming. Another criticalaspect is the variety of QoS support that should be provided,because some of the M2M applications can be throughputdemanding, other can be delay intolerant. Therefore, the re-quirements in terms of mandatory and optional C-MAC cyclefunctionalities, which should be supported in the M2M appli-cations, can be vastly different. In some M2M applicationsadvanced spectrum sharing (access/usage/scheduling) mecha-nisms should be employed, in other, high bandwidth controltraffic could be a necessity. In scenarios where the coexistencebetween M2M and cellular networks in coverage, capacity andinterference is critical, the C-MAC protocol should compriseall of the functionalities of the C-MAC cycle, i.e. the spectrumsensing, the spectrum sharing and the control channel.

VIII. CONCLUDING REMARKS

The Cognitive Radio and Cognitive Radio Networks areexpected to provide long-term solution to the spectrum scarcityproblem by smart exploitation of its current underutilization intime, space and frequency, while providing high level of protec-tion to the primary system. However, the achievable spectrumefficiency gains largely depend on a variety of factors rangingfrom the radio environmental heterogeneity, primary systembehavior and dynamics to secondary system’s operation limi-tations such as number of antennas, number of radios, single/multi-band operation, hardware constraints etc. The C-MAClayer and its mechanisms play key role in addressing thesechallenges, improving the secondary system performance and,ultimately, improving the overall spectrum efficiency.

The existing literature on the topic of C-MAC is exten-sive. Acknowledging the large number of C-MAC related as-pects, which make the C-MAC protocol engineering processinherently multidimensional, straightforward classification andsystematization of the proposed C-MAC solutions is not possi-

ble. As pointed out, there are several papers that address theproblem of C-MAC protocol classification, but they all lackgenerality since each of them focuses on a set of specific aspects(e.g. optimization, CRN infrastructure, spectrum access etc.)and renders the existing literature through them. To the best ofthe author’s knowledge there is still lack of classification thatpresents all generic and optional C-MAC related aspects in aunified and generic layout.

This survey aims at alleviating the deficiency and introduces,general, simple and modular C-MAC protocol classificationand systematization layout referred as C-MAC cycle. TheC-MAC cycle serves as generic and open layout that enablesmodular C-MAC protocol classification by identifying their un-derlying generic functionalities and differentiating the possibletechniques and mechanisms used to address various challengesand open issues regarding the generic functionalities. Themodularity of the C-MAC cycle also allows novel, emergingsolutions and concepts in C-MAC protocol engineering tobe easily merged and appropriately contextualized within it.Additionally, the C-MAC cycle recognizes and describes theimplications and influence of other CRN related aspects notdirectly related to the C-MAC protocol engineering (such asspectrum regulations and standardization) and identifies whichtechniques and solutions appropriately fit the limitations thatstem from such non-direct factors.

Upon closer investigation of the existing work on the topic,the general observation is that the main underlying idea generat-ing the C-MAC cycle is the requirement for mandatory supportof the three generic functionalities: radio environmental data ac-quisition (spectrum sensing/database); spectrum sharing; con-trol channel management. Therefore, the proposed C-MACcycle is heavily based on these three mandatory functionalitiesand the way they interact with each other and other networkentities, allowing the reader to fragment each particular C-MACprotocol and decide which set of techniques is used to supportthese functionalities. Such differentiation, besides providing acomplete understanding of the protocol operation, gives the


opportunity to devise new C-MAC protocols by combiningtechniques that provide the best performance (with respect topredefined design metrics) for the specific operational settings.In this sense, the majority of the survey focuses on reviewingthe state-of-the-art solutions and commonly utilized techniquesaddressing various spectrum sensing-specific, sharing-specificand control channel management-specific aspects of theC-MAC protocols. Fig. 19 outlines the operation of a C-MACprotocol, the interaction between its generic functionalities, andthe flow of information between different layers of the CRNprotocol stack.

Finally, the survey references and discusses the advantagesand drawbacks of most of the reviewed techniques, qualitativelydescribing their performance and implementation suitabilityproviding guidelines on how they can be optimally utilizedtowards designing highly efficient C-MAC protocols for highperformance CRNs.

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Liljana Gavrilovska (M’88–SM’02) currentlyholds the positions of full professor, Head of theInstitute of Telecommunications and the Center forWireless and Mobile Communications (CWMC) atthe Faculty of Electrical Engineering and Informa-tion Technologies, Ss Cyril and Methodius Univer-sity (UKIM) in Skopje. She is also the founderand head of the research Wireless NetworkingGroup. She is author/co-author of more than 150 re-search journal and conference publications and tech-nical papers, a co-author of the book Ad Hoc

Networking Towards Seamless Communications (Springer, 2006) andco-editor of the book Application and Multidisciplinary Aspects of WirelessSensor Networks (Springer, 2010). Her major research interest is concentratedon cognitive radio networks, future mobile systems, wireless and personal areanetworks, cross-layer optimizations, ad-hoc networking, traffic analysis andheterogeneous wireless networks.

Daniel Denkovski (S’11) received the Dipl.-Ing.and the M.Sc. degrees in telecommunications atSs. Cyril and Methodius University (UKIM) inSkopje, in 2008 and 2010, respectively. He is cur-rently finishing the Ph.D. studies at the Faculty ofElectrical Engineering and Information Technolo-gies at UKIM. Since the beginning of 2009, he hasbeen enrolled as Research Associate and memberof the Wireless Networking Group at the Facultyof Electrical Engineering and Information Technolo-gies. His research work in the areas of wireless com-

munications and networking, signal processing and radio resource managementin future wireless networks, has resulted in over thirty publications in relevantconferences and journals.

Valentin Rakovic (S’07) received the Dipl.-Ing.and the M.Sc. degree in telecommunications at theFaculty of Electrical Engineering and InformationTechnologies, Ss Cyril and Methodius University(UKIM) in Skopje, in 2008 and 2010, respectively.She is currently pusuing the Ph.D. degree and isa Teaching and Research Assistant at the Instituteof Telecommunications and member of the WirelessNetworking Group at FEEIT-Skopje. His researchwork focuses on the areas of cognitive radio and het-erogeneous networks, radio resource management,

signal processing, radio environment modeling and mapping, space divisionmultiple access techniques as well as prototyping of wireless networkingsolutions. He has (co-)authored more than 35 publications in internationalconferences and journals. He has extensive research experience having workedon several internationally funded projects, among them the EU FP7-eWall,EU FP7-ACROPOLIS, EU FP7-QUASAR, EU FP7-FARAMIR and NATORIWCoS and ORCA projects, as well as numerous domestic projects in hisresearch field.

Marko Angjelichinoski received the Dipl.-Ing. de-gree in telecommunications at Ss. Cyril and Method-ius University (UKIM) in Skopje, in 2011. He iscurrently finishing the M.Sc. studies at the Facultyof Electrical Engineering and Information Technolo-gies at UKIM. Since 2011, he has been enrolledas junior researcher and member of the WirelessNetworking Group at the Faculty of Electrical Engi-neering and Information Technologies. His researchinterests are in the areas of wireless communicationsand networking, statistical signal processing, estima-

tion and information theory.

Medium Access Control Protocols in Cognitive Radio Networks: Overview and General Classification

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