Energy conserving relay assistance for reporting users in cognitiveradio networks
M S SUMI* and R S GANESH
Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education,
Kumaracoil, Tamil Nadu, India
e-mail: [email protected]; [email protected]
MS received 28 August 2020; revised 18 April 2021; accepted 15 July 2021
Abstract. In Cognitive Radio (CR) networks, due to fading and shadowing the consistency of single user
sensing is deeply affected thereby degrading its detection performance. Cooperative Spectrum Sensing (CSS)
paves the way for improving the reliability of sensing in CR networks. In this work, an energy conserving relay
assistance method is proposed for modifying the soft fusion with improved Equal Gain Combining (EGC)
method to balance the energy consumption among the reporting users. The residual energies of the reporting
users are conserved in such a way that reporting is assisted by nearby non reporting users. The overall network
energy consumption is also not affected by the proposed method. Also the improved EGC soft fusion method is
compared and analysed with the conventional AND hard fusion method and the detection performance is
observed to be improved in the improved soft fusion method. In addition to energy conservation, complexity and
missed detection analysis are carried out for the proposed algorithm. MATLAB based simulations are performed
to compare and justify the algorithm proposed and to estimate the energy conservation analysis.
Keywords. Spectrum sensing; soft fusion; distance; signal-to-noise ratio; equal gain combining; energy.
1. Introduction
The deployment of 5G is expected to happen soon [1],
which will lead to enhanced connectivity in various aspects
of wireless sector. As a result, there will be a huge hike in
the number of devices being used. It will also result in
spectrum scarcity, since more spectrum resources are
required for meeting the demands of the increasing number
of devices. At the same time, according to Federal Com-
munications Commission (FCC), spectrum is not com-
pletely utilized and remains idle at certain locations and
durations [2]. This condition is referred to as spectrum
under-utilization. Therefore to address the issues of spec-
trum scarcity and under-utilization, an intelligent solution
like CR is being suggested [3]. It is defined as a smart
technology which follows the principle of Dynamic Spec-
trum Access (DSA). Its transceiver can sense the sur-
rounding radio frequency environment and find out the
unoccupied channels and instantly move into them. Once
the incumbent users or the Primary Users (PUs) arrive back
at their original licensed frequency band, then the cognitive
or Secondary Users (SUs) have to leave another available
free band without creating any interference to the PUs. This
process of DSA has been assisted by certain functionalities
such as spectrum sensing, decision, sharing and mobility
[4]. The purpose of spectrum sensing is to identify the
unused frequency bands that can be allotted to SUs during
the absence of PUs [5]. Since the comeback of PUs can
occur at any time, sensing has to be performed regularly.
But due to certain unfavourable conditions like fading,
shadowing, receiver uncertainty etc., the performance of
single user spectrum sensing is highly affected. Therefore
cooperative sensing is carried out, in which more than one
SU perform sensing and their local decisions are combined
together to make the final decision with or without the help
of a centralized Fusion Center (FC) [2]. The final decision
about channel availability can be obtained by using hard or
soft fusion rules.
While in the case of CSS, a trade-off arises between
spectrum efficiency as well as energy efficiency. That is as
the number of users increase, spectrum efficiency is
improved, simultaneously increasing energy consumption
of the individual cooperating users [6]. An improved EGC
based soft fusion method is proposed in [7] to address the
trade-off existing between both efficiencies in CR net-
works. The above said is modified in this work in such a
way that an energy conserving relay assistance method is
proposed in order to combat energy conservation of the
individual SUs in the improved EGC method. Here the
limited SUs with larger Signal-to-Noise Ratio (SNR) values
participating in results reporting can be assisted with non-
reporting relay users to conserve energy if required. The*For correspondence
Sådhanå (2021) 46:169 � Indian Academy of Sciences
https://doi.org/10.1007/s12046-021-01686-1Sadhana(0123456789().,-volV)FT3](0123456789().,-volV)
rest of the sections are described as follows. Related works
are explained in section 2, while section 3 demonstrates the
system model for CSS with the proposed algorithm. Sec-
tion 4 explains about the significance of fusion methods in
CSS. EGC soft fusion methods are explained along with the
proposed energy conserving relay assistance algorithm in
section 5. Simulations along with energy conservation
estimation and complexity analysis are demonstrated in
section 6. Detection performance of hard and soft fusion
methods are also compared in this section. Following which
conclusions and future perceptions are given in section 7.
2. Related works
To balance the spectrum and energy efficiencies in CR
networks, certain characteristics like energy consumption
minimization, sensing parameters optimization, fusion
rules’ analysis, etc., are given importance in literature [8].
An optimal soft fusion with Neyman - Pearson criterion is
being proposed by Ma et al [9] to improve detection. Also a
two bit softened hard fusion is further proposed to address
the trade-off between complexity and detection perfor-
mance. Shen and Kwak [10] proposed a linear soft fusion
method to balance interference and spectrum usage. The
sensing users are allocated with weights based on the
received PU energies and noise power levels. Authors have
proposed a selective weighted CSS scheme in [11], where
the users with higher SNR are involved in cooperation.
Various fusion schemes are studied in [12], among which
soft fusion is observed to have better performance with an
increase in complexity. While quantized fusion provides a
trade-off between performance and complexity. Particle
Swarm Optimization algorithm with Mini Max criterion is
proposed by El-Saleh et al [13], to minimize error proba-
bility and address convergence problem in soft fusion
methods. CSS with constant PU protection and constant SU
spectrum usability scenarios for EGC are analysed in [14].
Hossain and El-Saleh [15] proposed a soft fusion method
with binary genetic algorithm for improving bandwidth
utilization and detection results with faster convergence.
An adaptive sensing method based on user’s SNR value is
proposed in [16], where EGC fusion is employed for low
SNR users; else single user sensing is being used. A
reliability based fusion method presented in [17] is found
to minimize sensing error probability and improve per-
formance than AND and OR fusion methods. Also per-
formance is further enhanced with increase in the number
of SUs as different weights are assigned to SUs. The
sensed information is simultaneously transmitted to
FC in the sensing with EGC method, proposed by Hamza
et al [18].A soft linear CSS is studied in [19] and SNR based
algorithm is proposed and compared with the EGC method
to improve performance. Heterogeneous CR networks are
investigated in [20], where the SUs are employed with
different types of detectors and their final results are
combined with hard and soft fusion rules. Also an optimal
soft combiner is proposed, which has better performance
than EGC. The SU signals are weighted based on channel
quality and the detector type employed. Verma and Singh
[21] proposed a semi soft fusion method to overcome the
trade-off between bandwidth cost and sensing performance.
Here the test statistics is reconstructed at the FC from the
received bits. To overcome the noise uncertainty problem
and improve the performance, a soft fusion rule is proposed
by Farag and Mohamed [22], where the threshold levels at
the FC are dynamic. In [23], soft fusion is addressed with a
quantization based multi-bit sensing method, where the
user transmits multi-bit quantized data to FC. As a result,
performance with reduced overhead is achieved.
EGC is studied and applied for energy detection method
as well as Covariance Absolute Value method in [24] and
its performance is observed to be improved at low SNR.
Energy detection with cyclic redundancy check is per-
formed along with soft fusion in [25] and the sensed
information is retransmitted to FC if error is detected. The
detection is repeated and further erroneous SUs are dropped
from CSS. Improved performance and better throughput is
achieved even though sensing time required and overhead
are increased. Yuan et al [26] proposed a secure soft fusion
strategy, where maximum mean discrepancy is used to
distinguish malicious users from honest users. A combined
data and decision fusion is performed in [27], where the
least faded users having lower channel estimation error are
following hard fusion while the remaining SUs perform soft
combining. CSS is performed in the presence of multiple
PU in [28], where a quantization sensing method is pro-
posed to report the status of these multiple PUs to FCs by
SUs and final decisions of all PUs are obtained.
A multi-bit fusion is employed in [8] to maximize energy
efficiency, where the sensing parameters are optimized
along with the optimization of the number of cooperating
users, quantization bits to be received at FC and global
threshold for detection. Kumar et al [29] proposed a double
threshold based hard soft combining method, enabling log
likelihood detector. Here both sensing and reporting chan-
nel SNR are considered for weight estimation. Maximiza-
tion of energy efficiency by optimizing fusion rule and
reducing the number of SUs are investigated in [30] for non
fading AWGN and frequency flat fading channels. An
improved EGC method is proposed in [7] to enhance the
detection performance in CSS, where the number of local
decisions forwarded to FC is reduced, thereby saving
reporting energy.
3. System model
A centralized CSS based CR network representing our
proposed scheme is presented in figure 1. It has N SUs
sensing the availability of PU and the local decisions are
169 Page 2 of 11 Sådhanå (2021) 46:169
reported as per the proposed algorithm to a single FC. Each
SU is assumed to have V antennas, where V � 1. Each
SU’s position is not the same and they have different SNR
(� j) with the PU channel. Each SU participates in spectrum
sensing and the local decisions of different users are com-
bined in FC using fusion rules to make the global decision
regarding channel status. In order to achieve an interference
free spectrum access, the network is assumed to follow an
interweave access method [31]. Here, the channel is uti-
lized by the SU, only when it is observed to be free. This
can be obtained by performing sensing by the SUs in order
to identify the absence of the incumbent user in its
respective channel.
4. Significance of fusion rules in cooperativespectrum sensing
Sensing with energy detection is performed by each SU to
sense the PU signal, as stated by equation (1). Test statistics
is estimated from the received signal to be compared with
the threshold value [4]. Here the transmitted input signal
from PU is assumed to be a random input signal which is a
real valued Gaussian signal. It is combined with additive
white Gaussian noise signal.
The signal received by the SU is given below.
s nð Þ ¼ g nð Þy nð Þ þ r nð Þ � � � �H1
r nð Þ � � � �Ho
�ð1Þ
where n = 1,2,3,…LHere y(n) is the received PU signal with g(n) as the
amplitude gain of the channel. While r(n) is additive whiteGaussian noise with zero mean and variance rr
2. L is the
total number of samples at the received signal. The pres-
ence and absence of PU signal is indicated by hypotheses
H1 and Ho respectively [4]. The test statistics is calculated
for every SU as in equation (2).
X ¼ 1
L
XLn¼1
s nð Þj j2 ð2Þ
In the case of hard fusion based CSS, the test statistics of
individual SUs are compared with the determined value of
threshold and if found greater than that threshold, the
channel is stated as busy [32]. A single bit data indicating
the channel status is reported to FC, where all received
decisions are combined using any one of the methods of
hard fusion to determine the global decision. Whereas in
soft fusion, the estimated test statistics at each user are
forwarded to the FC, where they are combined as per the
soft fusion method and compared with their respective
threshold estimated in order to determine the global status
of the channel [12].
Some of the most popularly used hard fusion techniques
are AND, OR, Majority, Chair-Varshney methods etc.
While EGC, Square Law Selection (SLS), Maximum Ratio
Combining (MRC), Selection Combining (SC) etc., are
some of the popular soft fusion methods employed [12, 21].
Comparing hard and soft fusion methods, soft fusion
affords better detection performance. But at the same time,
it requires more overhead complexity and bandwidth as it
has to transmit the entire test statistics or energy signal
instead of forwarding a single bit of data to FC via the
control channel [32]. Equation (3) given below describes
the PU status of AND hard fusion method, where Bj is the
single bit decision of jth SU. If the SUs observe the PU to be
present, then the channel is stated as busy [12].
Figure 1. System model for centralized CSS with energy conserving relay assistance method.
Sådhanå (2021) 46:169 Page 3 of 11 169
ifPNj¼1
Bj ¼ N;H1
else;Ho
8<: ð3Þ
Also, the global detection and false alarm probabilities
of AND fusion [32] are listed by equations (4) and (5).
Qd ¼YNj¼1
Pdj ð4Þ
Qfa ¼YNj¼1
Pfj ð5Þ
where Pdj and Pfj are the probabilities of detection and
false alarm for the jth SU. Also the global missed detection
probability is expressed as in equation (6).
Qmd ¼ 1� Qd ð6Þ
5. Soft fusion to improve detection performanceand energy efficiency
Among the various soft fusion methods, EGC is one of the
simplest methods, where the test statistics from individual
sensing users are transmitted to the FC. At the FC, these
signals are combined with equal weights and the combined
value obtained is the global test statistics. This value will be
compared with the final threshold to determine the PU
status [23]. The global test statistics is calculated as men-
tioned in equation (7).
Eegc ¼XNj¼1
Ej ð7Þ
Two important parameters justifying the performance
of sensing in CR networks are detection and false alarm
probabilities [33]. Lower the value of false alarm prob-
ability, higher the possibility for spectrum utilization.
But, it can also rise the number of missed detections
during which the sensing user could not detect a PU
using the channel, leading to collision and thereby
interference to both users. The global detection and false
alarm probabilities for EGC are stated as in equations (8)
and (9)
Qd ¼Tegc �
PNj¼1 1þ � j
� �r2r
� �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
LPN
j¼11þ� jð Þr4r
!vuut0@
1A
0BBBBBB@
1CCCCCCA
ð8Þ
Qfa ¼Tegc �
PNj¼1 r
2r
� �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
LPN
j¼1r4r
r !0BBBB@
1CCCCA ð9Þ
where � j is the SNR of jth SU, rr2 is the noise variance and
Tegc given by equation (10) is the estimated global thresh-
old and Ej is the energy at the jth user [7, 23].
Tegc ¼ Q�1 Qfa
� � ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2
LPN
j¼1 r4r
s ! !þXNj¼1
r2r ð10Þ
Here Q(.) and Q-1(.) are defined as the standard Q and
inverse Q functions respectively, where Q xð Þ ¼1ffiffiffiffiffiffiffi2pð Þ
p R1x e
�t2
2
� �dt [34]. In MRC type of soft fusion, the
energy value at each user will be provided with an indi-
vidual weight corresponding to their SNR value. These
weighted signals are combined at the FC and compared
with the relative threshold value to determine the PU status
[12]. Although better detection is observed in MRC
method, it has certain drawbacks like higher complexity,
need of larger bandwidth for reporting as well as prior
knowledge of channel state information [18], which makes
EGC method more adaptable.
5.1 Improved EGC soft fusion rule
In order to overcome the trade-off between detection per-
formance and bandwidth conservation, an improved EGC
method is proposed in [7]. The test statistics of an indi-
vidual sensing user is forwarded to FC only if its SNR is
found to be higher than the average SNR of the network.
Better detection probability is obtained, as sensing results
of low SNR SUs are not reported to be combined to make
the global decision. As the number of reporting users is
reduced, reporting energy can also be saved. The average
value of SNR of all sensing users in the CR network is
calculated as mentioned in equation (11).
SNRavg ¼ 1
N
XNj¼1
� j ð11Þ
The global test statistics Eimpegc is estimated by com-
bining the received high SNR test statistics at FC. It is
compared with the threshold Timpegc to determine the
availability of PU in the channel. Equations (12) and (13)
state the expressions for estimating Eimpegc and Timpegc.
Eimpegc ¼XMj¼1
Ej ð12Þ
169 Page 4 of 11 Sådhanå (2021) 46:169
Timpegc ¼ Q�1 Qfa
� � ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2
LPM
j¼1 r4r
s ! !þXMj¼1
r2r ð13Þ
M is the number of high SNR test statistics reported to
FC, where M B N.
5.2 Proposed energy conserving relay assistancealgorithm for improved EGC method
In improved EGC method [7], the SUs with high SNR
value alone participate in reporting test statistics to FC.
Even though energy conservation is achieved with
respect to the entire CR network, individual users which
are involved in reporting to FC, face energy consumption
imbalance. Hence to overcome this issue, relay users can
be considered. The non-reporting SUs present in the CR
network, which are both nearer to the FC as well as to the
reporting users that are far away from the FC are allowed
to act as supporting relay users. An alternative reporting
path is established between the reporting SUs and FC
through the selected relay SUs. The improved EGC
algorithm as in [7], is further modified using Algorithm 1
with this energy conserving relay assistance method. As
in Algorithm 1, the long distance reporting users which
require relay support are determined based on the aver-
age distance value of reporting users from FC. The relay
users are predicted with respect to their distance from
FC. The long distance reporting users are matched with
the relay users to find out the relevant one, which can
report the test statistics to the FC with minimum energy
consumption. Hence energy consumption of the reporting
Figure 2. Flowchart representing energy conserving relay assistance algorithm.
Sådhanå (2021) 46:169 Page 5 of 11 169
process is shared among the reporting and the relay users,
thereby securing the reporting SU from running out of
energy. The flowchart in figure 2 explains the process in
Algorithm 1.
Algorithm 1. Energy conserving relay assistance
method
1. Determine the average SNR of sensing users SNRavg
2. Compare the single user SNR with SNRavg
3. Identify the reporting and non-reporting SUs
4. Determine distance of each reporting user from FC
RFCj
5. Find the mean distance between reporting users and FC
as RFCavg
6. Identify the long distance reporting users R by compar-
ing RFCj with RFCavg
7. Determine distance of each non-reporting user from FC
NFCj
8. Find the mean distance between non-reporting users and
FC as NFCavg
9. Identify the suitable relay users RL by comparing NFCj
with NFCavg
10. Match R and RL and identify the appropriate RL for each
R to minimize energy consumption
5.3 Energy consumption analysis
One of the important aspects paving the way for better
network characteristics in CR networks is obtaining
energy efficiency. It is the ratio of network throughput to
total energy consumption [30]. It is measured in bits/
joule. Hence reducing energy consumption is much
essential not only for improving the performance of the
entire network, but also for the survival of the individual
sensing users. Energy consumption includes the total
energy spend on sensing, results reporting and data
transmission. Also energy consumption increases with
the increase in the number of cooperating users. In the
method of improved EGC [7], since the number of
reporting users are reduced, corresponding reporting
energy can be saved. But there is an issue of the reporting
users to drain out of energy, if their respective residual
energies are low with respect to their reporting distances
from FC. Reporting energy of jth SU [35] is given as in
equation (14).
Erj ¼ Tu � Prfcj ð14ÞTF ¼ Ts þ Tr þ Tt ð15Þ
where Tu is the reporting time for a single user as per the
time frame (TF), stated by equation (15). Also, the total
reporting duration Tr is N times Tu. The total time frame is
comprised of the sensing and reporting energies along with
the transmission slot (Tt). Prfcj is the reporting power of jth
SU to FC. The reporting energy consumed by the alternate
path with relay for jth user is given by equation (16).
Errj ¼ Tu � Prlj
� �þ Tu � Plfcj
� � ð16Þwhere Prlj is the reporting power of jth reporting user
which requires relay assistance to its respective relay
user and Plfcj is the reporting power of the corresponding
relay user to the FC. The reporting power of a SU is a
function of distance to be transmitted [36] and is pro-
portional to the reporting distance. It is defined as in
equation (17).
Pr ¼ G � Rd ð17Þwhere Pr is the reporting power of any user and Rd is its
corresponding reporting distance.
6. Simulation results
In this section, the simulation of the proposed energy
conserving relay assistance method is performed and
energy consumption analysis is carried out along with
complexity analysis. Also, the improved EGC fusion
method is simulated along with AND fusion method to
compare the detection performance of hard and soft fusion
methods. Missed detections are analysed for different false
alarm values.
6.1 Simulation of energy conserving relayassistance method
Energy detection based CSS is employed with 1000 sam-
ples for each SU. Simulations are performed in MATLAB
with 1000 Monte Carlo runs. The analysis is carried out for
varying number of SUs and a single PU and FC. These
users are assumed to be employed with single antenna each.
Figure 3 analyses the Receiver Operating Characteristics
(ROC), plotting the detection performance against false
alarm probability comparing the conventional and
improved EGC methods as in [7] for 20 SUs at SNR
ranging between -30 and -11 dB.
Relay assistance is provided to the improved EGC
method [7] based on the simulation of energy conserving
relay assistance method (Algorithm 1). Table 1 gives an
overview of reporting and relaying users with respect to the
simulation of algorithm for 20 SUs at SNR range of -30 to -
11 dB.
From table 1, it is observed that out of 20 sensing users, 7
SUs will be capable of reporting to FC. Among which 4
SUs are found to be far away from the FC and require relay
assistance to conserve their respective reporting energies.
Among the 13 non reporting users, 8 SUs are observed to
be capable of assisting as relay users. The relay user
169 Page 6 of 11 Sådhanå (2021) 46:169
suitable for the corresponding reporting user is estimated as
in Algorithm 1 and is given in table 2.
As in table 2, an alternate reporting path can be estab-
lished for those 4 reporting users which are found to be far
away from the FC, to report their decisions to the FC.
Thereby with the help of relay users, these reporting users
are able to conserve their reporting energies. The reporting
duration for each user in this soft fusion is assumed as
0.0008 [35]. The reporting power of individual users and
relay users, which depends on their respective reporting
distances are determined as in equation (17), where G is
assumed to be 0.001. Energies consumed by reporting users
with and without relay assistance are estimated using
equations (16) and (14) and the obtained values are repre-
sented in table 4. It also analyses the reporting energy
consumed by the network by implementing the energy
conserving relay assistance method. The reporting power
estimated is displayed in table 3, where equation (17) is
employed.
From the above tables, it is observed that, the reporting
users are benefitted in such a way that the energies con-
sumed by them for reporting decisions are being reduced.
Their respective residual energies are being conserved with
the assistance of relay users, who spent a little amount of
their residual energies to forward the decisions to the FC.
Also it has been observed that, the increase in total energy
consumed for forwarding results by these users via the
alternate relay path are too small and negligible, thereby
maintaining overall network energy usage.
6.2 Complexity analysis
Even though the soft fusion methods are noted to have
better detection performance than hard fusion methods,
they have larger overhead and increased complexity. This is
Figure 3. ROC representing detection performance of improved EGC and EGC methods [7]
Table 1. Available number of reporting and relaying users.
No. of
sensing
users
No. of reporting
users as per
improved EGC
algorithm [7]
No. of reporting
users requiring
relay assistance
No. of non-
reporting users
capable of
relaying
20 7 4 8
Table 2. Reporting user - relay user pair.
Reporting user R Relaying user RL
1 6
2 3
3 1
4 2
Table 3. Reporting power estimation (in milliwatts).
Reporting
user id
Reporting power
of user without
relay assistance
Reporting
power of user
with relay
assistance
Reporting
power of
corresponding
relay user
1 208.325 81.7125 128.3125
2 198.9875 91.3375 107.6625
3 196.6125 93.0375 103.5875
4 186.0875 80 106.1
Sådhanå (2021) 46:169 Page 7 of 11 169
because the SUs are forwarding their entire observed
energies to FC. In improved EGC methods [7], the number
of reporting users is reduced, thereby resulting in increased
energy conservation and bandwidth efficiency. In this
extended work, as the reporting distances of individual
users are minimized, their reporting overhead complexity is
addressed. The reporting distances of users for two condi-
tions, that is without and with relay assistance are estimated
and displayed in table 5.
From table 5, it is observed that the reporting distances
of users are reduced once relay support is being
employed. As a result their individual reporting com-
plexity is minimized.
6.3 Comparison of improved EGC methodwith AND fusion method
Improved EGC and conventional EGC methods are com-
pared with AND fusion method and their simulation results
are illustrated in figure 4, for 15 users at SNR range of -25
to -11 dB. From the results obtained it is observed that, the
soft fusion methods have higher detection performance than
AND fusion method. Table 6 demonstrates the comparison
of detection performance of hard and soft fusion methods
for different number of SUs at different SNR ranges.
From the results obtained, it is observed that both hard
and soft fusion methods have improved detection perfor-
mance at higher SNR ranges. Also, soft fusion has better
performance for different number of users and under dif-
ferent SNR conditions.
Table 4. Energy consumption analysis (in millijoules).
Reporting
user id
Energy consumed for user
without relay assistance
Energy consumed for user
with relay assistance
Energy consumed by
corresponding relay user
Total energy consumed by
alternate relay path
1 0.16666 0.06537 0.10265 0.168024
2 0.15919 0.07307 0.08613 0.159205
3 0.15729 0.07443 0.08287 0.15731
4 0.14887 0.06400 0.08488 0.14889
Table 5. Reporting distance estimation (in metres).
Reporting
user id
Reporting
distance of user
without relay
assistance
Reporting
distance of
user with relay
assistance
Reporting
distance of
corresponding
relay user
1 208325 81712.5 128312.5
2 198987.5 91337.5 107662.5
3 196612.5 93037.5 103587.5
4 186087.5 80000 106100
Figure 4. Comparison of detection performance of improved EGC and conventional EGC with AND fusion methods.
169 Page 8 of 11 Sådhanå (2021) 46:169
6.4 Missed detections and interferencemanagement in soft fusion methods.
From the above figures and tables, it is understood that at
lower values of false alarm probability, the detection per-
formance is observed to be lower. This also increases the
number of missed detections. When the PU channel is
falsely detected to be free and is utilized by a SU for data
transmission, then collisions take place, creating interfer-
ence between both secondary and incumbent users. When
compared with the hard fusion and conventional EGC
methods, improved EGC method has lower missed detec-
tions even at lower values of false alarm probability. Also
these missed detections are lower at higher SNR conditions.
Figure 5 illustrates the comparison of the missed detections
for the hard and soft fusion methods. Here the number of
missed detections is observed to increase with the decrease
in the values of false alarm. Also, the improved EGC
method has reduced missed detections than the conven-
tional methods. As the users satisfying the SNR criteria
alone forward their sensed information, inappropriate
detections are avoided, reducing collisions and interference
between secondary and incumbent users.
7. Conclusion
This work proposes an energy conserving relay assistance
method for modifying the existing improved EGC method,
in such a way that the energy consumed for reporting
decisions to FC is shared by both the reporting and non-
reporting users. As a result, the residual energy of the
reporting SUs are saved from getting drained out without
affecting the overall energy consumption of the network.
Reduction in energy consumption as well as reporting
distances paves the way for reduced complexity for the
individual reporting users. Also, improved EGC soft fusion
method is observed to have increased detection perfor-
mance than hard fusion methods for varying SNR condi-
tions. When compared with the hard fusion and
conventional EGC methods, improved EGC method has
lower missed detections even at lower values of false alarm
Table 6. Comparison of detection performance of hard and soft fusion methods.
No.of SUs SNR range (dB) AND fusion method (Qd) Improved EGC method (Qd)
10 (- 30 to - 21) 0.0000 0.0000 0.0000 0.0000 0.0170 0.0690 0.1330 0.2170
0.0000 0.0002 0.0015 0.0066 0.3360 0.4560 0.5960 0.7380
0.0595 1.0000 0.8670 1.0000
10 (- 20 to - 11) 0.0000 0.0000 0.0001 0.0008 0.5910 0.7720 0.8990 0.9440
0.0034 0.0138 0.0431 0.1217 0.9670 0.9770 0.9940 0.9970
0.3058 1.0000 1.0000 1.0000
20 (- 30 to - 11) 0.0000 0.0000 0.0000 0.0000 0.6070 0.7900 0.9010 0.9560
0.0000 0.0000 0.0001 0.0009 0.9700 0.9890 0.9940 0.9990
0.0171 1.0000 1.0000 1.0000
Figure 5. Comparisons of missed detection probabilities of improved EGC and conventional EGC with AND fusion methods.
Sådhanå (2021) 46:169 Page 9 of 11 169
probability. Also, these missed detections are lower at
higher SNR conditions. As a part of future enhancement,
this relay assistance work can be further modified in such a
way that the entire network energy consumption as well as
power required are further reduced, bringing energy effi-
ciency to the complete CR network. Also, the proposed
energy conserving soft fusion method can be analysed for
channels with adverse fading conditions and employing
multiple receiver antennas at the SUs. The reporting
channel condition of the relay users can also be explored to
reduce further missed detections and interference to users.
List of symbolsBj Single bit decision of jth SUEegc Global test statistics of EGC fusion
Eimpegc Global test statistics for improved EGC
Ej Local test statistics of jth SUErj Reporting energy of jth SU to FC
Errj Reporting energy of R-RL path to FC
G Constant
Ho Binary hypothesis denoting PU is absent
H1 Binary hypothesis denoting PU is present
L Number of samples in the PU signal received
M Number of results forwarded to FC in improved
EGC method
N Number of cooperating SUs
NFCavg Mean distance between non-reporting users and
FC
NFCj Distance of jth non-reporting user from FC
Pdj Probability of detection of jth SUPfj Probability of false alarm of jth SUPlfcj Reporting power of jth relay to FC
Pr Reporting power for any user
Prfcj Reporting power of jth SU to FC
Prlj Reporting power of jth reporting user to relay
Q(.) Standard Q function
Q-1(.) Inverse Q function
Qd Global detection probability
Qfa Global false alarm probability
Qmd Global missed detection probability
R Long distance reporting users requiring relay
assistance
Rd Reporting distance for any user
RFCavg Mean distance between reporting users and FC
RFCj Distance of jth reporting user from FC
RL Relay users
SNRavg Entire CR network’s average SNR
SUj jth SUTegc Global threshold for EGC fusion
TF Total time frame
Timpegc Final threshold for improved EGC
Tr Reporting slot
Ts Sensing duration
Tt Data transmission slot
Tu Reporting duration of a single user
V No. of antennas in each SU transceiver
X Test statistics to be compared with threshold
� j SNR of jth SUrr
2 Variance of noise
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