Efficient Load-Aware Channel Allocation in Wireless Access Networks

Hindawi Publishing CorporationJournal of Computer Networks and CommunicationsVolume 2011, Article ID 972051, 13 pagesdoi:10.1155/2011/972051

Research Article

Efficient Load-Aware Channel Allocation inWireless Access Networks

George Athanasiou, Ioannis Broustis, and Leandros Tassiulas

Department of Computer and Communications Engineering, University of Thessaly, 38221 Volos, Greece

Correspondence should be addressed to Ioannis Broustis, [email protected]

Received 30 October 2010; Revised 7 February 2011; Accepted 2 March 2011

Academic Editor: Abdallah Shami

Copyright © 2011 George Athanasiou et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Dense deployments of hybrid WLANs result in high levels of interference and low end-user throughput. Many frequency allocationmechanisms for WLANs have been proposed by a large body of previous studies. However, none of these mechanisms considersthe load that is carried by APs in terms of channel conditions, number of affiliated users as well as communication-load, inconjunction. In this paper, we propose LAC, a load-aware channel allocation scheme for WLANs, which considers all the aboveperformance determinant factors. LAC incorporates an airtime cost metric into its channel scanning process, in order to capture theeffects of these factors and select the channel that will provide approximately maximum long-term throughput. We evaluate LACthrough extensive OPNET simulations, for many different traffic scenarios. Our simulations demonstrate that LAC outperformsother frequency allocation policies for WLANs in terms of total network throughput by up to 135%.

1. Introduction

The growing demand for high-throughput wireless Internetconnectivity has enabled the deployment of thousandsof WLANs in urban areas, during the last decade. This,however, has resulted in increased amounts of interferenceand contention among cochannel access points (APs) [1].As a consequence, the end-users (affiliated clients withthose APs) end up enjoying very low throughputs inthe long term. Towards addressing this problem, variousperformance enhancement mechanisms have been proposed,a set of which considers the efficient allocation of frequencybandwidth (i.e., the available channels) to the APs and theirclients (i.e., the AP cells) [2–6]. However, each of thesefrequency allocation studies considers only a subset of theparameters that affect the performance of the network.

(a) Number of associated clients. The number of theclients that are distributed in a network affects thecommunication interference that is present in thenetwork. In other words, the higher the number ofthe clients associated with an AP, the higher theinterference effect in the neighboring cells.

(b) Channel conditions. The channel conditions reflectthe projected interference (SINR) and thereby theachievable transmission rates for a specific channel.

(c) Communication load. The communication load(amount of traffic that the APs must forward tothe associated clients or to the network) affects theachievable throughput. The use of channels that aremore capable to serve the load in the cells is expectedto improve the network throughput.

There are no studies that embrace all these parameters undera common frequency selection framework; therefore thepreviously proposed solutions are efficient only when certainconditions are met (as we discuss in detail, in Section 2).

As our contribution in this paper, we propose LAC,a load-aware channel assignment scheme that discoversthe channel assignment that approximately maximizes theAP throughput, that is, the sum of throughputs achievedby all its affiliated clients. LAC employs the airtime costmetric [7] (the airtime cost metric was proposed in [8] asa routing metric (RM-AODV) and in [7, 9] for optimaluser association in 802.11 networks). The airtime costdirectly reflects the environmental conditions around each

2 Journal of Computer Networks and Communications

device (AP or client), in terms of interference and contentionexperienced due to concurrent transmissions. By adoptingthis metric, LAC discovers the most appropriate channel forevery AP in a distributed fashion, by measuring (a) boththe downlink and uplink channel conditions in terms ofsupported transmission rates and packet error probabilityand (b) the number of affiliated clients with every AP. Asa result, the AP has a unified knowledge with regard to thequality of all its downlink and uplink connections.

With LAC, every AP and its clients perform a sequentialscanning on all the available channels and collect measure-ments with regard to the above metrics. Furthermore, theyexchange these measurements to compute the cumulativeairtime cost for the AP cell that they belong to, and forevery channel. After a set of iterations they select the channelwherein the airtime cost is minimal; this is the channelthat will provide approximately the maximum long-termthroughput, since the throughput can be represented as aninverse function of the airtime cost. In [9] we have analyzedthe relationship of the airtime metric with the uplink channellong-term throughput. In this paper, we extend this analysisin order to identify the relationship of the airtime metricwith the total throughput in the cell (for both uplink anddownlink channels).

We evaluate LAC through extensive simulations inOPNET [10]. We simulate the network behavior with thefollowing types of traffic: (a) fully-saturated, downlink UDPtraffic, where the APs send traffic to their associated clients,(b) fully-saturated, bidirectional UDP traffic (where thesource-destination pairs are chosen randomly), (c) VoIPtraffic and (d) video traffic. We compare our schemeagainst the frequency selection approach, proposed in [2]and against two other simple channel allocation schemes.We present LAC’s predominance in terms of the totalnetwork throughput, average packet dropping,and averagetransmission delay.

The rest of the paper is structured as follows. Section 2provides the relevant previous work on channel allocationfor WLANs. Section 3 describes the airtime metric thatwe employ, based on which LAC discovers the channelwith the maximum long-term throughput. In Section 4, wepresent the design of LAC. We evaluate our protocol throughsimulations, in Section 5. Finally, Section 6 concludes thispaper.

2. Related Studies

In this section, we discuss the most relevant previous workon efficiently assigning channels to APs in 802.11 wirelessLANs and in 802.11 wireless mesh networks. A large set ofstudies on channel selection in WLANs exists; to the best ofour knowledge, however, none of these mechanisms capturesthe total actual throughput at the AP cell for every particularchannel.

The LCCS (least congested channel search) method [11]was the first effort towards allocating a set of availablechannels to wireless devices. With LCCS, devices (e.g., APs)periodically scan the set of available channels and select

the one with the lowest levels of contention (as the namesuggests). However, there are many topological scenarioswhere LCCS is unable to capture the total interference inthe channel, as explained in [4]. Similarly, Leith and Clifford[12] propose a self-managed distributed channel selectionscheme, wherein each AP passively measures the receivedpower from the packets transmitted by neighbor APs.

Along similar lines, Kauffmann et al. in [2] propose adistributed frequency selection algorithm, which is provedto minimize the global interference in the network. Simplyput, minimizing the total interference can result in improveduser throughput. Towards addressing this objective, each APmeasures the total received power from all neighbor APs forevery channel and selects the channel with the minimumtotal power. This is performed at each AP by measuring theRSSI of the received beacon frames from all the neighborAPs at every channel. The authors show that their proposedalgorithm manages to converge to the global optimumof the optimization criterion, that is, the minimizationof interference across the entire network. However, thisalgorithm does not consider the number of clients in thenetwork; it assumes purely downlink saturated traffic andthat all APs have affiliated clients. Moreover, the channel withthe minimum total interference does not guarantee maximalthroughput at the cell. We compare LAC against the protocolin [2], in Section 5.

Moreover, the work in [6], by Mishra et al., belongs toa set of studies that propose a distributed channel hoppingmechanism. The mechanism in [6], MaxChop, provideshigher levels of fairness among users. Channel hopping,however, requires tight synchronization between AP andclients, while it is difficult to implement efficiently with off-the-shelf hardware. Note that the channel switching and thesubsequent restoration of traffic at the new channel may takefrom 700 to 1000 msec [13]; this is prohibitive in terms ofincurred overhead.

Lee et al. [14] take into account the expected trafficdemand points in the network. Their channel allocationstrategy seeks to assign frequencies in such a way that thesignal strength at these demand points is maximized. As afurther step, Rozner et al. in [3] also consider the currenttraffic demands at the WLAN. In particular, they show that,taking into consideration the current traffic demands at APsand clients, the quality of the channel assignment can begreatly improved.

Furthermore, centralized channel allocation algorithmshave been proposed in [4, 5, 15]. Mishra et al. [4] proposea frequency allocation scheme, wherein clients play a largerole in the decision for the best channel. Their proposedapproach opts to perform joint load balancing and frequencyallocation. However, the approach is based on conflict graphcoloring and cannot be directly implemented in a distributedsetting. Leung and Kim [5] present a formulation of thechannel assignment problem for 802.11 WLANs, which isthen proven to be NP complete. Then, they design andanalyze a heuristic algorithm that attempts to minimizethe effective channel utilization for the bottleneck APs. Theauthors in [15] propose a novel framework to model the loadof WLAN cells considering intercell interference. They also

Journal of Computer Networks and Communications 3

present a frequency planning algorithm which is designed onthe basis of the aforementioned load model. Their algorithmprovides fair service to its users, while preserving highnetwork utilization.

Efficient channel selection is essential in 802.11 meshdeployments too, for minimizing contention and interfer-ence among cochannel devices. However, the requirementsof a channel allocation policy there are different from achannel allocation policy applied in WLANs. A criticalrequirement for the efficient routing of packets is theidentification and use of interference-limited wireless links.Therefore, intermediate mesh hops along a route need tooperate in frequencies, where contention and interferenceare as low as possible, especially in highly dense meshdeployments.

Alicherry et al. [16] study the joint channel allocation androuting problem, assuming that traffic demands and networktopology are known. They present an LP formulation ofthe problem, and they propose a centralized algorithm thatmaximize the aggregate throughput.

Raniwala and Chiueh propose in [17] a tree-based mesharchitecture, called Hyacinth, where local channel usage andchannel load information is exchanged, and the channelallocation is based on this information. They approach thejoint problem of channel assignment and routing in wirelessmesh networks.

Ramachandran et al. [18] propose a measurement-basedcentralized approach to provide efficient channel allocationfor radios. They perform channel-to-interface assignmentbased on channel reuse possibilities which in turn depend oninterference.

Wang and Garcia-Luna-Aceves [19] present JARS, ajoint channel assignment, routing, and scheduling scheme,which incorporates the efficiency of underlying channelassignment and scheduling information into the routingmetric calculation so that the route with the maximal jointspatial and frequency reuse is selected.

The authors in [20] develop a protocol that assigns con-tiguous channels with the goal of evenly spreading the loadacross the multiple channels. Neighboring nodes greedilyadjust their channel ranges according to channel conditionsto achieve an overall pattern of partially-overlapping band-widths that maximizes the network throughput.

Lastly, an interesting work [21] addresses the threatsto channel assignment in wireless mesh networks resultingfrom node misbehaviors and presents a generic verificationframework to detect such misbehaviors. The authors developa concrete verification scheme based on this framework andan existing distributed channel assignment schemes.

Most of the previous approaches assume the availabilityof a global network view. Our work differentiates from theseapproaches by providing efficient channel selection in adistributed manner.

Our work embraces the aforementioned parametersunder a common frequency selection framework, since thereare no approaches in the literature that consider all thefactors that affect the channel selection, in conjunction. Anew metric, called airtime cost, is used in our frameworkin order to discover the most appropriate channel for every

AP in a distributed fashion. The airtime cost is able tocapture the effects of the factors discussed in previousapproaches and therefore to select the channel that willprovide approximately maximum long-term throughput inthe network.

3. A Metric for Channel Allocation

In this section, we present the metric that is used by LACin order to effectively select the most appropriate channel inevery cell.

The metric that is used in our load-aware channelallocation scheme is called airtime metric, and it is anapproximation of per packet latency (as shown in [9] forthe uplink channel). The airtime metric was first discussedin the 802.11s [8] standard, for the purposes of load-awarerouting (RM-AODV routing protocol). This metric reflectsthe load on a wireless router (AP) in terms of the averagedelay a transmission of a unit size packet experiences. RM-AODV, which is the default routing protocol in 802.11s-based wireless mesh networks, employs the airtime metricin order to provide routes with the minimum total airtimecost. We adopt this metric in LAC for the purposes of ourproposed channel selection functionality. In particular, weconsider the airtime cost for the individual AP-client links; wemeasure the total airtime cost for the current cell, and we useit as a metric to select an appropriate channel (as we discusslater).

Formally, the airtime metric of station i ∈ Ua, where Ua

is the set of stations associated with AP a that communicateusing channel c, is given as

Cia,c =

[Oca + Op +

Bt

Ra,ci

]1

1− ecpt. (1)

In (1), Oca is the channel access overhead, Op is the protocoloverhead, and Bt is the number of bits in the test frame(the transmission of test frames is necessary, in order toderive values for the computation of the airtime cost). Somerepresentative values for these constants, for 802.11g, are:Oca + Op = 1.25 ms and Bt = 8224 bits. Furthermore,Ra,ci and ecpt are the current transmission rate and frame-

error rate, respectively, in Mbps, for the test frame size Bt inchannel c. In other words, the estimation of ecpt correspondsto transmissions of standard-size frames Bt at the currenttransmit bit rate Ra,c

i .The average airtime cost (in one direction: uplink or

downlink) of AP a with Na users, that operates on channelc is

Ca,c = 1Na

Na∑i=1

[Oca + Op +

Bt

Ra,ci

]× 1(

1− ecpt)

=⎡⎣ 1Na

Na∑i=1

(Oca + Op

)+

1Na

N∑i=1

Bt

Ra,ci

⎤⎦× 1(

1− ecpt) .

(2)


The Case for Uplink Traffic. First, we discuss the relation-ship of the airtime metric with the average uplink net-work throughput. In [22, 23], the average uplink throughputin a single cell environment is calculated under saturationand decoupling approximations. The saturation approxima-tion states that there are always packets backlogged on everyuser. Meanwhile, with the decoupling approximation it isassumed that when there are N users, the aggregate attemptprocess of (N − 1) nodes is independent of the back-offprocess of any given node. We consider the simple case, whenall nodes have the same back-off parameters, each node isthe transmitter for a single flow, and all packets have equallengths. As derived in [22], the single flow average uplinksaturation throughput θ(a, i, c) in channel c, of node i thatis associated with AP a is given by (3),

1θ(a, i, c)

= 1

μ(1− μ

)Nα−1Lai x

ai

×⎡⎣1+Naμ

(1−μ)Na−1

⎛⎝T0−Tc+

1Na

Na∑q=1

LaqRa,cq xaq

⎞⎠

+(

1− (1− μ)Na−1

)Tc

⎤⎦,

(3)

where μ is the attempt rate (probability) in the equilibrium,Na =

∑Nq=1 x

aq is the number of STAs (Stations) associated

with AP a (xai is a binary value that takes value 1 in caseSTA i is associated with AP a and 0 otherwise), T0 is thefixed overhead with packet transmission, Tc is the fixedoverhead for an RTS (Request To Send) collision, and Laiis the size of the packet of STA i when transmitting to APa. Due to the exponential back-off behavior of the nodesand the decoupling approximation, it can be shown that theattempt probability of a node accessing the channel can bedetermined in terms of a given collision probability γ as

G(γ) =

∑Kk=0 γ

k∑Kk=0 γkbk

, (4)

where K is the maximum number of attempts allowed bythe protocol, and bk is the mean back-off at the kth attempt.Meanwhile, the probability of collision of an attempt by anode is given by

Γ(μ) = 1− (1− μ

)Na−1 (5)

due to the decoupling approximation [22]. The behavior ofthe system at equilibrium is governed by the solution of thefixed-point equation

γ = Γ(G(γ)). (6)

The solution of this equation yields the collision probability,from which the attempt rate in the equilibrium μ can bedetermined from (4).

In addition, Lai /θ(a, i, c) is the average per-packet delay atthe equilibrium that STA i sends to AP a in channel c and isgiven by

Laiθ(a, i, c)

= 1

Naμ(1− μ

)Na−1 + (T0 − Tc)

+1− (1− μ

)Na

Naμ(1− μ

)Na−1 Tc +1Na

Na∑q=1

LaqRa,cqxaq.

(7)

The first three terms in (7) represent the delay dueto channel contention and protocol overheads, while thelast term corresponds to the average transmission timeof an L-length packet by a node in the cell. Clearly, thetransmissions of RTS (request to send), CTS (clear tosend), DATA, and ACK (acknowledgment) frames may alsoget corrupted not only due to collisions but also due tochannel errors. The work in [24] assumes that the wirelesschannel can be modeled by an appropriate Gilbert modelwith known transition probabilities. In wireless networkswith dynamically changing conditions, however, such anassumption is not practical. Therefore, in our work, thecurrent frame error rate in channel c, ecpt is measured byusers and AP. For each transmission attempt, the packetwould be in error due to channel errors with probabilityecpt. Clearly, the average number of attempts until successfultransmission would be 1/(1 − ecpt). In addition, for eachtransmission attempt, the average delay that is expressed in(7) is experienced. The product of 1/(1−ecpt) and the averageper-packet delay of a client approach the average delay untilsuccessful transmission of this client:

Laiθ(a, i, c)

× 11− ecpt

=[

1

Naμ(1− μ

)Na−1 + (T0 − Tc) +1− (1− μ

)Na

Naμ(1− μ

)Na−1 Tc

+1Na

Na∑q=1

LaqRa,cqxaq

⎤⎦× 1

1− ecpt.

(8)

Comparing (2) and (8) we can clearly see that the averageuplink airtime cost of AP a is an approximation of the averageper-packet delay expressed in (8). In (2), Oca + Op is thedelay due to channel contention and protocol overhead, and1/Na

∑Nai=1(Bt/R

a,ci ) is the average transmission delay of a Bt-

length packet (we can assume that Lai = Bt).

The Case for Downlink Traffic. We now discuss the rela-tionship of the airtime metric with the average downlinknetwork throughput. As derived in [2], the long-term averagedownlink saturated throughput for each client in cell a andfor channel c is

θ(a, c) = Ma∑Naq=1 1/Ra,c

q, (9)


where Ma is the fraction of time that AP a acquires thewireless channel. Therefore, the per-packet transmissiondelay towards each client, in channel c, is

Laiθ(a, c)

= 1Ma

Na∑q=1

LaqRa,cq. (10)

Now, given the packet error rate ecpt, the average number oftransmission attempts to successfully deliver a packet to thereceiver, in channel c, is 1/(1 − ecpt) (as explained before).Consequently, the lower bound of the expected transmissiontime for an STA to receive a packet successfully is

Laiθ(a, c)

× 11− ecpt

=⎡⎣ 1Ma

Na∑q=1

LaqRa,cq

⎤⎦× 1

1− ecpt. (11)

Finally, comparing (2) and (11) we claim that the airtime costmetric approximates the average downlink per-packet delaythat each STA faces. In particular, Ma is directly related tothe Oca + Op overhead that is considered by the airtime costmetric. Hence, the average airtime cost is a representativemetric that captures the downlink channel performance, inaddition to the uplink one.

The main contribution of our work is that our schemecaptures the performance of the available channels that canbe used by the APs in the network, measuring the averageairtime cost for both uplink and downlink (airtime cost forAP a, in channel c):

Cca = C

upa,c + Cdown

a,c , (12)

and applies a channel allocation methodology where thechannel with the minimum Cc

a is chosen. This also approxi-mates the maximum throughput in the cell.

Various studies have shown that the number of erro-neously received packets increases and the transmission ratedecreases, in the presence of interfering cells in the network[2, 25]. Our proposed airtime metric takes into account thepacket error rate as well as the transmission rate; hence,it reflects the performance at a particular communicationchannel. In the next section, we describe our channelallocation protocol; we explain how the airtime metric is usedin order to optimize the allocation of the available channelsand improve the network throughput.

4. LAC: Our Channel Allocation Scheme

In this section, we describe LAC, our load-aware channelselection mechanism. Our analysis from the previous sectionclearly shows that the airtime metric reflects the performanceof the WLAN in terms of AP throughput. Hence, determin-ing the channel with the lowest airtime cost will provideapproximately the maximum long-term throughput to theclients within a cell. This is the target of LAC. LAC is AP-centric, that is, the channel choice is made by the AP at everycell. Note that the clients also play a very important role inthe channel decision, by informing the AP with regard to theuplink channel conditions. This way the AP has knowledge

with regard to both the downlink and uplink states. Ina nutshell, LAC operates at both the AP and client ends.At every scanned channel, the AP and its clients measurethe downlink and uplink channel properties and exchangethem through their control and data transmissions. Thisinformation is subsequently used by the AP to select thechannel with the minimum cumulative airtime cost. Weexplain the scanning operations of LAC in what follows.

Step 1 (computing the downlink airtime cost). At the nomi-nal start of LAC, AP Ai of cell i initiates the downlink airtimecost calculation and informs its clients by setting a specialbit into the beacon frame. Then it calculates the averagedownlink airtime cost for the links with its clients, throughthe link performance-measurement procedure, described indetail in [7]. In brief

(i) Ai calculates the frame error rate ecpt for each down-link communication on channel c, based on previousmeasurements (e.g., by measuring the percentage ofthe dropped packets in a time window),

(ii) Ai stores the transmission rate RAi,c for each downlinkcommunication on channel c,

(iii) Ai computes the airtime cost for each downlinkcommunication on channel c, and

(iv) Ai computes the average downlink airtime cost onchannel c.

Step 2 (computing the uplink airtime cost). The clientsof Ai read the airtime cost bit from Ai’s beacon frametransmissions, and they further calculate their individualuplink costs [7]

(i) STAs calculate the frame error rate ecpt on channel cin their uplink communication, based on previousmeasurements (e.g., by measuring the percentage ofthe dropped packets in a time window).

(ii) STAs store their uplink transmission rates on channelc.

(iii) STAs calculate their individual uplink airtime costson channel c and inform the Ai.

(iv) Ai calculate the average uplink airtime cost onchannel c.

STAs may include their costs into their measurement reportmessages towards Ai, as per [26]. Alternatively, they canalso piggy-back this information through their data packetstransmissions towards Ai. In this work we follow the latterapproach; we discuss this choice later. Through this step, Ai

receives information about the uplink channel qualities fromall its clients.

Step 3 (deciding if the current channel is appropriate). Ai

receives the client reports and computes the average airtimecost for both uplink and downlink, for the access level. If thisis higher than a predefined threshold, T , then Ai remains inthe same channel; otherwise it initiates a channel discoveryprocess.


Step 4 (computing the cumulative airtime cost at the nextavailable channel). Ai and its clients switch to the nextchannel and repeat Steps 1 to 3. If all available channels havebeen visited, Ai finally selects the channel with the minimumaverage airtime cost (for both uplink and downlink).

4.1. LAC Properties. We now discuss some properties of ourLAC mechanism.

(1) Band dwell duration. Calculating CcAi

in cell i forchannel c requires that Ai and its clients conductlink measurements in order to derive values for thetransmission rate and frame error rate, as explainedin the previous section. Towards collecting accuratevalues for these parameters, a sufficient amount oftime of residing at a particular channel is to beconsumed by all devices of cell i. Note, however,that (a) the measurement collection process for everychannel is performed through data exchange betweenAP and clients, and therefore traffic keeps flowingin the network even during channel scanning. Inother words, with LAC, traffic does not stop flowingbetween AP and clients, as with other channelallocation protocols (e.g., [2, 12], etc.). (b) In typicaltoday’s WLANs, where clients are statically locatedmost of the time, the levels of interference are notfluctuating to a large extent. Thus, LAC does notneed to be executed frequently, as we discuss below.Hence, one can expect that due to (a) and (b) above,the projected overheads because of the channel dwellduration are minimal.

(2) Convergence and frequency of invocation. The set ofSteps 1–4 belong to an iterative process, where theset of available channels is rescanned by the APs,until convergence has been reached. This is becausewhenever AP Ai decides upon a certain channel ci,the cumulative airtime cost for the neighbor cells ofAi will likely change for channel ci; this will force theAPs of those cells to keep scanning for a potentiallybetter channel than ci. Our simulations in Section 5show that in a static deployment with 20 numberof APs and 40 number of clients, convergence isreached rather quickly. We expect that in a moredynamic topology, where clients join or deassociatefrom the network, the invocation of LAC would bemore frequent, since the violation of threshold Twould occur more often. This problem is not soprominent with the schemes proposed in [2, 12],since those mechanisms do not consider the potentialtransmissions of clients. Due to this, however, theseapproaches yield a much poorer performance thanLAC, as we discuss in the following section. As far asthe computation of the threshold T is concerned, wemust mention that this is a system designer decision.T depends on the topology of the network and thecommunication load. Therefore, the system designermust adapt the threshold T according to the systemcharacteristics. An automatic mechanism that can be

used in order to adapt threshold T and provide abalanced network operation is presented in [9].

(3) Embedding the airtime cost value into control and dataframes. With LAC, clients piggy-back their individualuplink airtime costs into their data transmissionstowards the AP. As we mentioned earlier, another wayof sending this information to their AP is throughprobe response frames, or special management reportframes [26]. Note however that, as reported in [27],such control frames are not always received intact bytheir destinations, since they are not acknowledged.Therefore, the converge of mechanisms that dependon these frames may be delayed, since the AP doesnot manage to collect accurate information withinthe channel dwell duration [27]. Thus, we considerthat the airtime cost value is repeatedly piggy-backedinto every data packet transmission, for reliability.

5. Evaluating Our Load-Aware ChannelAllocation Scheme

In this section, we evaluate our load-aware channel allo-cation scheme through extensive OPNET [10] simulations.We compare our scheme against the frequency selectionapproach, proposed in [2] and against two other simplechannel allocation schemes. We present LAC’s predominancein terms of the total network throughput, average packetdropping, and average transmission delay.

5.1. Simulation Set-Up Details. We have implemented LACin OPNET [10], taking into account the 802.11 protocoloperations. We have modified the beacon and data framesof 802.11 to facilitate the information exchange process thatour protocol design requires. The clients and the APs areuniformly (at random) distributed in a 1000 m × 1000 msimulation area. All nodes use a default transmit powerof 20 dBm. We simulate the network behavior with thefollowing types of traffic: (a) fully saturated, downlink UDPtraffic, where the APs send traffic to their associated clients,(b) fully saturated, bidirectional UDP traffic (where thesource-destination pairs are chosen randomly), (c) VoIPtraffic, and (d) video traffic. We repeat our simulations withboth 802.11a and 802.11g modes of operation. We comparethe network performance of LAC against the following.

(i) Single-channel assignment (SC). This is a very com-mon approach, where the APs use a preselecteddefault channel (e.g., channel 6). The network devicemanufacturers design their products to operate on adefault channel when they are turned on. Therefore,we believe that this situation is very common inreal deployed wireless networks. We have performedseveral measurements, and we have observed theaforementioned operational characteristics.

(ii) Random-channel allocation strategy (RC). The ownersof the APs, in absence of build-in mechanismsthat capture the interference in the environment,


select randomly a communication channel. This is acommon approach too.

(iii) Gibbs-based Frequency Selection (GFS). We considera scheme that is very similar to the one proposed in[2]; we call this scheme GFS. With GFS, each AP iter-atively scans all the available channels, and greedilyselects the channel with the (per channel) minimumaggregate received power (i.e., the minimum sum ofRSSI values plus noise) from all neighbor APs. Thedesign of GFS assumes purely downlink saturatedtraffic; that is, packets are assumed to flow only fromthe APs towards their clients (please see [2] for moredetails).

We evaluate the efficiency of LAC in selecting the mostappropriate channel by measuring the total achieved networkthroughput, the average end-to-end delay, and the averagedropped data packets in the network.

5.2. Simulation Results and Observations. We present oursimulation experiments and the interpretations thereof, inwhat follows.

5.2.1. Applying Downlink UDP Traffic. To begin with, weopt to compare the performance with LAC against theperformance with the other approaches, with downlinktraffic (e.g., online movie downloading). For this, we applysaturated downlink UDP traffic in a network with 20APs and 40 STAs, all uniformly randomly distributed inthe area. In other words, we assume that the APs alwayshave packets, to send to their associated clients. We usethe default UDP packet size (1500 bytes). Figure 1 depictsthe average total network throughput when there are 11(802.11a) and 3 (802.11g) orthogonal channels available.The best performance is achieved by LAC, which uses theairtime metric to capture the cell performance at everychannel. GFS underperforms because the number of theassociated clients is not considered; the latter assumesthat the imposed interference on an AP comes only fromits neighbor APs, while the interference at the client isassumed to be approximately equal as that at the AP. Notehowever that in this scenario GFS does not achieve muchpoorer throughput than LAC, since the experiment involvesdownlink traffic only. In other words, the above assumptionof GFS is weak in a purely downlink traffic scenario.Nevertheless, our load-aware scheme performs better thanthe other three channel selection approaches. In particular,LAC outperforms GFS by 8% and RC by 59% when 802.11ais used, and by 20% and 104%, respectively, when 802.11gis used. The improvement is higher with 802.11g networksbecause there are only 3 orthogonal channels available (lessthan the number of the available channels in 802.11a)and therefore, a random channel allocation policy is morelikely to result to increased amounts of interference inthe network (the channel reusability is increased). Ourmechanism minimizes the interference in this environmentand improves significantly the network performance. On theother hand, in 802.11a networks the improvement is not so

SC RC GFS LAC0

5

10

15

20

25

30

35

Th

rou

ghpu

t(M

bps)

802.11a

802.11g

Figure 1: Total network throughput with saturated downlink UDPtraffic.

high because the channel reusability of a random allocationpolicy is limited (as compared to 802.11g).

5.2.2. Applying Bidirectional UDP Traffic between RandomSource-Destination Pairs. We are also interested in thenetwork performance with LAC, in more realistic (thandownlink-only) traffic scenarios (e.g., gaming applications).In this case, each client sends saturated UDP traffic toanother randomly selected client in the network. Note thatthe client-destination may be associated to a different APthan the client-source (in our simulations, APs are connectedthrough a wireline Ethernet network, and they use theEthernet interface to exchange packets with other cells).Hence, both uplink and downlink UDP traffic takes placein every cell (uplink traffic in the client→AP (source)link and downlink traffic in the AP→ client (destination)link). Figure 2 depicts the average total network throughput,achieved with the different channel selection approaches.We observe that GFS underperforms LAC to a large extent(LAC improves network throughput achieved by GFS by 27%in 802.11a and by 47% in 802.11g). This is because GFSdoes not take into account the fact that also clients may besending traffic towards their APs; hence, the uplink channelconditions and the load of each communication channel arenot considered by GFS. Note that in our simulations wehave clients being interfered by neighboring APs. Besides, theinterference may be different than the one experienced bytheir affiliated APs. Hence GFS’s assumption does not holdhere. Finally, we observe that LAC improves the total networkthroughput as compared to RC, by 70% in 802.11a and by135% in 802.11g.

Next, we seek to observe the throughput with LAC asthe network density increases, in terms of number of clients.For this, we progressively increase the number of clients(uniformly, at random, distributed) from 5 to 70 in thenetwork, while we maintain the same number of APs (20).


SC RC GFS LAC0

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rou

ghpu

t(M

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Figure 2: Total network throughput with both-directions UDPtraffic.

5 10 15 20 25 30 35 40 45 50 55 60 65 700

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t(M

bps)

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Figure 3: Total network throughput versus number of clients.

Figure 3 depicts the throughput results for the four channelselection strategies. We observe that the performance withLAC is similar to the one with the other 3 policies, whenthe number of clients (and therefore the load) is low.However, as the load increases, LAC manages to providemuch higher throughputs due to its load-aware channelallocation strategy.

Moreover, we measure the average total networkthroughput as we increase the number of APs, from 10 to80 (meanwhile, we deploy twice the number of clients: 10APs—20 clients, 20 APs—40 clients, etc.). Figure 4 depictsthe throughput gains with LAC. We observe that our schememanages to scale much better than all other 3 approaches.In other words, the maximum achievable total network

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Figure 4: Total network throughput versus number of APs.

throughput is reached when the network includes 10 moreAPs for the case of LAC than in the case of GFS. The reasonbehind this is that LAC is able to capture and handle theinterference in an efficient way and provide approximatelymaximum network throughput. LAC “stretches” the networkcapabilities.

5.2.3. Simulating LAC with VoIP Traffic. In order to observethe performance of our protocol with delay-sensitive appli-cations, we utilize varying, parallel, end-to-end VoIP trafficsessions. The simulation setup is the same as for the previousbidirectional experiments; that is, VoIP traffic is exchangedamong clients in our network—hence we again have bothuplink and downlink traffic at every cell. Figures 5 and 6present the network performance with VoIP.

In particular, Figure 5 depicts the average end-to-enddelay of VoIP packet transmissions. We observe that LACachieves low end-to-end delays, due to its sophisticated chan-nel allocation strategy. GFS achieves quite good performancein low load communication conditions. However, as thenumber of the supported VoIP sessions increases, the chan-nel setting with GFS is unable to efficiently support them.Unfortunately, GFS considers just the downlink channelcharacteristics, and therefore it is enabled to support highlyloaded VoIP traffic (where both uplink and downlink trafficis introduced in the system). Finally, Figure 6 shows the aver-age number of dropped data packets due to channel errorsand contention. The performance of LAC is impressive, sincepacket dropping is kept in very low levels, as compared to theother strategies.

5.2.4. Simulating LAC with Video Traffic. We measure theperformance of our protocol when video traffic is introducedin the network. In order to simulate MPEG-4 video trafficover a WLAN, we have imported video traces to OPNET.Specifically, we have obtained MPEG-4 video traces from


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d-to

-en

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RCSC

Figure 5: Average end-to-end delay with VoIP traffic.

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/s)

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LACGFS

RCSC

Figure 6: Average dropped data with VoIP traffic.

a 60 m movie: Jurassic Park, which is available in [28].Then we have created a traffic profile in OPNET where atransmission interval of 20 ms is introduced. Table 1 containsmore information about the parameters of the video trafficthat we use in our experiments. We keep the same simulationsetup as for the previous experiments, and we measure theperformance of LAC compared to SC, RC, and GFS whilethe number of the video connections (sessions) that aresupported in the network varies.

Figure 7 depicts the average end-to-end delay of videodata transmission. The load-aware channel allocation policyintroduced by LAC keeps the transmission delay at low levels,while the performance of SC and RC is disappointing. Theperformance of GFS is very good in low load conditions,but in high load conditions the performance drops. As we

Table 1: Video traffic parameters.

Parameter Value

Coding format QCIF (176 × 144)

Transmission interval 20 ms

Frame rate 25 frames/sec

Frame structure IBBPBBPBBPBB

Mean frame size 0.77 KByte

Compression ratio YUV : MP4 49.46

2 4 6 8 10 12 14 16 180

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Tran

smis

sion

dela

y(s

)

LAC

GFSRCSC

Figure 7: Average end-to-end delay with video traffic.

can see in Figure 7, when we support more than 8 videoconnections we face huge transmission delays with SCand RC in the system. The packet size varies in videotraffic (in contrast to CBR traffic), and the amount of thetransmitted data is large. Therefore, the interference andthe packet congestion is increased in the network. LACtakes into account the communication interference andprovides a balanced network operation. Figure 8 shows theaverage delivery ratio in the video data packet transmission,and Figure 9 depicts the average amount of propped datawhile the number of the video connections increases. Thesesimulation results confirm that SC and RC are unable toefficiently support more than 8 or 10 video connectionsin the network and that LAC provides the best networkperformance.

5.2.5. Supporting Different Types of Traffic. Now we optto measure the performance of the compared schemes ina simulation scenario which approaches real life networkoperational conditions. We make good use of the capabilitiesthat the OPNET simulation environment offers, supportingin this way several applications in parallel and thereforetesting the adaptability of our protocol under different trafficdemands in the network. Specifically, we introduce thefollowing applications: HTTP (5 clients—heavy browsing),E-mail (8 clients), FTP (5 connections—500 Kb file size),


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GFSLAC

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(%)

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Figure 8: Average delivery ratio with video traffic.

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pped

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(bit

s/s)

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GFSLAC

Figure 9: Average dropped data with video traffic.

VoIP (8 connections), and video (5 connections—MPEG-4traces).

In Figure 10, we observe the average total networkthroughput when the aforementioned applications are sup-ported. LAC uses the airtime metric to capture the cellperformance at every channel and achieves the best perfor-mance. In particular, LAC outperforms GFS by 34% andRC by 114%, and therefore LAC can efficiently support theaforementioned applications in parallel. Figure 11 depictsthe percent of the successfully transmitted packets in thenetwork. LAC achieves high average delivery ratio whileSC and RC are enable to support the traffic demands inthis simulation scenario. The performance of GFS is quitegood especially in low load communication conditions.Its performance drops when the load in the networks is

0 5 10 15 20 25 30 35 40 45

SC

RC

GFS

LAC

Throughput (Mbps)

Figure 10: Total network throughput supporting different types oftraffic.

0 10 20 30 40 50 60 70 80 90 100

SC

RC

GFS

LAC

Delivery ratio (%)

Figure 11: Average delivery ratio supporting different types oftraffic.

increased to high levels, and therefore the average deliveryratio is average.

5.2.6. LAC Convergence. In Section 4, we provided a dis-cussion with regards to the achievable convergence of ourscheme. In order to visualize the discussion points, weexamine how quickly LAC converges, and the propagationeffect that is caused by a potential performance variationin the network when LAC is applied (we discuss laterwhy a performance variation happens). We keep the samesimulation settings with the previous experiments and weapply bidirectional UDP traffic in the network.

In the first scenario, we simulate a network with 20 APsand 40 STAs, all uniformly randomly distributed in the area.We apply LAC, and a convergence is reached after a finitenumber of iterations. Then, we randomly choose an AP inthe network and we vary its communication characteristics(e.g., we manually change the channel that the AP uses) in


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Figure 12: LAC convergence.

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Number of APs

Con

verg

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tim

e(s

)

Figure 13: Convergence time versus number of APs.

order to force it to re-execute LAC. In other words, theperformance of this AP exceeds the threshold T that wehave introduced in LAC. Therefore, LAC must be re-executedto “redeem” the efficient network performance. Figure 12depicts the realtime total network throughput. We observethat the network performance has reached a stable state (LAChas converged). Then, we randomly choose an AP and wemanually change the communication channel that is used(selected by LAC). In this way we modify the interference inthe network. In Figure 12, we can observe the adaptation ofLAC when this performance variation happens. After a smallnumber of iterations the network reaches again a stable state(LAC reconverges). The time that passes till convergence inour simulation scenario is close to 20 ms.

Furthermore, we measure the time that passes untilconvergence, as we increase the number of APs from 10to 80 (meanwhile, we deploy twice the number of clients:10 APs—20 clients, 20 APs—40 clients, etc.). We repeatthe previous experiment while we vary the number of the

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paga

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)

Figure 14: Propagation effect when the AP that “fires” theexecution of LAC is placed at the center of the network.

APs in the network, in order to measure the LAC averageconvergence time in different topologies and communicationscenarios (we run several experiments to get accurateresults). Figure 13 demonstrates that the average convergencetime is less than 20 sec when we support up to 40 APs in thenetwork. After that point, the average convergence time issignificantly increased due to the large number of the APsthat must execute LAC and share the available channels inthe network.

Finally, we opt to observe the propagation effect that iscaused by our scheme in the network. As we have describedbefore, our mechanism reaches a stable state after a finitenumber of iterations (convergence). The network performsefficiently in this state. However, a potential performancevariation in the network may trigger the re-execution of ourmechanism. The performance variation may be caused byincreased interference (new STAs/APs are turned on in thenetwork or the transmission power of the current STAs/APschanges), variations in the network topology (mobile STAs),variation in the environment (buildings, walls), and so forth.After convergence, LAC turns into monitor mode in orderto “act” in case that the performance exceeds the thresholdT that is defined by the system designer. The re-executionof LAC in one or more cells may initiate a “domino” effect.In other words, the new channel set that outcomes from there-execution of LAC in a cell may affect the performance ofthe neighboring cells. Therefore, LAC must be re-executed inthe neighboring cells in order to select a better channel setthat will improve their performance. We call this “domino”as propagation effect. It is true that the threshold T controlsthe sensitivity of a potential LAC propagation effect inthe network. We support the fact that the system designermust manage a tradeoff that is present during the executionof LAC. High threshold values affect the sensitivity andthe efficacy of LAC, and low threshold values may triggerfrequent unavailing LAC re-executions (propagation effect).Consequently, the system designer must adapt the thresholdT according to the system characteristics. An automatic


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Number of APs

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)

Figure 15: Propagation effect when the AP that “fires” theexecution of LAC is placed at the edge of the network.

mechanism that can be used in order to adapt threshold Tand provide a balanced network operation is presented in[9].

We measure the number of the APs that re-execute LACdue to a possible fluctuation in the network performance.We examine three scenarios where the AP that “fires” theexecution of LAC is placed at the center, at the edge andat random in the network. In Figures 14, 15, and 16 weobserve the propagation effect of the aforementioned threescenarios as we increase the number of APs, from 10 to 80(we run several experiments in order to get accurate results).As we can see, the highest propagation effect is present whenthe AP that “set off” the execution of LAC is located at thecenter of the network. That happens because at the center ofthe network the number of the APs that are affected by theincreased interference is large and LAC must be executed byall these APs. Contrarily, when the AP is located at the edgeof the network, the propagation effect is quite low.

6. Conclusions

In this paper, we propose LAC, a load-aware channelselection mechanism for 802.11-based WLANs. LAC adoptsthe airtime cost metric, which was originally proposed inthe 802.11s standard, and which provides an estimationfor the average packet transmission delay. LAC employs achannel scanning procedure that converges to the channelwith the minimum average airtime cost (for both uplinkand downlink), thus managing to provide high cell through-put. We compare LAC against 3 other channel selectionapproaches, and we show that it outperforms all of them,for different traffic scenarios and network densities. Efficientchannel allocation is very important in dense deploymentsof WLANs, where the interference in increased and the end-user throughput is very low. LAC is able to capture thecommunication environment characteristics and effectively

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Number of APs

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Figure 16: Propagation effect when the AP that “fires” theexecution of LAC is placed at random of the network.

provide a distributed channel allocation methodology whichguarantees high network performance.

Acknowledgment

This work has been supported by EU through the FP7 projectOPNEX 224218.

References

[1] A. Akella, G. Judd, S. Seshan, and P. Steenkiste, “Self-management in chaotic wireless deployments,” in Proceedingsof the 11th Annual International Conference on Mobile Comput-ing and Networking (MOBICOM ’05), pp. 185–199, September2005.

[2] B. Kauffmann, F. Baccelli, A. Chaintreau, V. Mhatre, K.Papagiannaki, and C. Diot, “Measurement-based self orga-nization of interfering 802.11 wireless access networks,” inProceedings of the 26th IEEE International Conference onComputer Communications (INFOCOM ’07), pp. 1451–1459,May 2007.

[3] E. Rozner, Y. Mehta, A. Akella, and L. Qiu, “Traffic-awarechannel assignment in enterprise wireless LANs,” in Proceed-ings of the 15th IEEE International Conference on NetworkProtocols (ICNP ’07), pp. 133–143, October 2007.

[4] A. Mishra, V. Brik, S. Banerjee, A. Srinivasan, and W. Arbaugh,“A client-driven approach for channel management in wirelessLANs,” in Proceedings of the 25th IEEE International Conferenceon Computer Communications (INFOCOM ’06), April 2006.

[5] K. K. Leung and B. J. Kim, “Frequency assignment for IEEE802.11 wireless networks,” in Proceedings of the 58th IEEEVehicular Technology Conference (VTC ’03), vol. 3, pp. 1422–1426, October 2003.

[6] A. Mishra, V. Shrivastava, D. Agrawal, S. Banerjee, and S.Ganguly, “Distributed channel management in uncoordinatedwireless environments,” in Proceedings of the 12th AnnualInternational Conference on Mobile Computing and Networking(MOBICOM ’06), pp. 170–181, Los Angeles, Calif, USA,September 2006.


[7] G. Athanasiou, T. Korakis, O. Ercetin, and L. Tassiulas,“Dynamic cross-layer association in 802.11-based meshnetworks,” in Proceedings of the 26th IEEE InternationalConference on Computer Communications (INFOCOM ’07),pp. 2090–2098, May 2007.

[8] IEEE 802.11s: Wireless LAN Medium Access Control (MAC)and Physical Layer (PHY) Specifications: Simple EfficientExtensible Mesh (SEE-Mesh) Proposal.

[9] G. Athanasiou, T. Korakis, O. Ercetin, and L. Tassiulas, “Across-layer framework for association control in wireless meshnetworks,” IEEE Transactions on Mobile Computing, vol. 8, no.1, pp. 65–80, 2009.

[10] “OPNET-Radio/Wireless Models,” http://www.opnet.com.[11] J. Geier, “Assigning 802.11b access point channels,” in Proceed-

ings of the WiFI Planet Conference, 2002.[12] D. J. Leith and P. Clifford, “A self-managed distributed channel

selection algorithm for WLANs,” in Proceedings of the 4thInternational Symposium on Modeling and Optimization inMobile, Ad Hoc and Wireless Networks (WiOPT ’06), pp. 1–9,Ireland, April 2006.

[13] B. Vedantham, S. Kakumanu, S. Lakshmanan, and R. Sivaku-mar, “Component based channel assignment in single radio,multi-channel ad hoc networks,” in Proceedings of the 12thAnnual International Conference on Mobile Computing andNetworking, (MOBICOM ’06), pp. 378–389, September 2006.

[14] Y. Lee, K. Kim, and Y. Choi, “Optimization of AP placementand channel assignment in wireless LANs,” in Proceedingsof the 27th Annual IEEE International Conference on LocalComputer Networks (LCN ’02), Tampa, Fla, USA, November2002.

[15] S. J. Han, Y. Bejerano, and M. Smith, “A novel frequencyplanning algorithm for mitigating unfairness in wirelessLANs,” in Computer Networks, vol. 54, no. 15, pp. 2575–2590,October 2010.

[16] M. Alicherry, R. Bhatia, and L. Li, “Joint channel assign-ment and routing for throughput optimization in multi-radio wireless mesh networks,” in Proceedings of the AnnualInternational Conference on Mobile Computing and Networking(MOBICOM ’05), pp. 58–72, 2005.

[17] A. Raniwala and T. C. Chiueh, “Architecture and algorithmsfor an IEEE 802.11-based multi-channel wireless mesh net-work,” in Proceedings of the 24th Annual Joint Conference ofthe IEEE Computer and Communications Societies (INFOCOM’05.), pp. 2223–2234, March 2005.

[18] K. N. Ramachandran, E. M. Belding, K. C. Almeroth, andM. M. Buddhikot, “Interference-aware channel assignment inmulti-radio wireless mesh networks,” in Proceedings of the 25thIEEE International Conference on Computer Communications(INFOCOM ’06), April 2006.

[19] X. Wang and J. J. Garcia-Luna-Aceves, “Distributed jointchannel assignment, routing and scheduling for wireless meshnetworks,” in Computer Communications, vol. 31, no. 7, pp.1436–1446, May 2008.

[20] E. Chai and K. G. Shin, “M-Polar: channel allocation forthroughput maximization in SDR mesh networks,” in Proceed-ings of the 29th IEEE International Conference on ComputerCommunications (INFOCOM ’10), March 2010.

[21] M. Kim and P. Ning, “SeCA: a framework for secure channelassignment in wireless mesh networks,” Computer Communi-cations Elsevier Journal, vol. 34, no. 4, pp. 567–576, 2011.

[22] G. Bianchi, “Performance analysis of the IEEE 802.11 dis-tributed coordination function,” IEEE Journal on SelectedAreas in Communications, vol. 18, no. 3, pp. 535–547, 2000.

[23] A. Kumar, E. Altman, D. Miorandi, and M. Goyal, “Newinsights from a fixed point analysis of single cell IEEE 802.11

WLANs,” in Proceedings of the 24th Annual Joint Conferenceof the IEEE Computer and Communications Societies (INFO-COM ’05), pp. 1550–1561, March 2005.

[24] N. Gupta and P. R. Kumar, “A performance analysis of the802.11 wireless LAN medium 13 access control,” Communi-cations in Information and Systems, vol. 3, no. 4, pp. 279–304,2004.

[25] D. Niculescu, “Interference map for 802.11 networks,” inProceedings of the 7th ACM SIGCOMM Internet MeasurementConference (IMC ’07), pp. 339–350, October 2007.

[26] IEEE 802.11 WG, Wireless Medium Access Control (MAC) andPhysical Layer (PHY) Specifications: Specification for RadioResource Measurement, IEEE 802.11k/D3.0, IEEE, New York,NY, USA, 2005.

[27] I. Broustis, K. Papagiannaki, S. V. Krishnamurthy, M. Falout-sos, and V. P. Mhatre, “Measurement-driven guidelines for802.11 WLAN design,” IEEE/ACM Transactions on Network-ing, vol. 18, no. 3, pp. 722–735, 2010.

[28] “MPEG-4 and H.263 Video Traces for Network Per-formance Evaluation,” http://www.tkn.tu-berlin.de/research/trace/trace.html.

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