Enhancement of cooperative spectrum sensing employing genetic algorithm and noise power estimation

rts from a population of randomly generated individuals and happens
in generations. In each generation, the fitness of every individual in the population is
evaluated; multiple individuals are selected from the current population and modified
to form a new population. The new population is then used in the next iteration of
the algorithm. Commonly, the algorithm terminates when either a maximum number of
generations has been produced, or a satisfactory fitness level has been reached for the
population.
 GA starts with the generation of population of pops randomly generated solutions.
Each solution is called a chromosome or an individual. The chromosome includes all
the variables that are used in the optimization objective function. In this case, the
chromosome is as follows
 T
 Chromosomei = wi = [w1, w2, ··· , wM ] ,
 where wi is the i-th individual and M is the number of SUs.
 The fitness function value for the i-th individual is defined as follows
 Fi = Pd(wi) (10)
where Pd stands for probability of detection.
 The main procedure of the GA includes selection, crossover and mutation:
 Selection
 In order to selection, the best chromosomes are chosen for reproduction through
crossover and mutation. The larger the fitness value, the better the solution obtained. In
this paper “Roulette Wheel selection” method has been used. Equation 11 defines the
probability of selecting the i-th individual or chromosome pi
 Fi
 pi = Ppops (11)
 i=1 Fi
 Through elitism operation, the chromosomes with maximum probability of detection
value will be transferred to the next generation.
 Crossover
 Crossover is a genetic algorithm operator that is used to vary the variables in the
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Chuyên san Công nghệ thông tin và Truyền thông - Số 10 (06-2017)
individuals from one generation to the other. A uniform random number generator has
been used to select the row numbers of chromosomes as mother (ma) or father (pa). It
starts by randomly selecting a variable in the first pair of parents to be the crossover
point. Fig. 3 illustrates crossover operation where α is the crossover point and β is a
value randomly chosen in the range [0, 1].
 Fig. 3. GA crossover operation
 For parent 1 (mα) → offspring 1 (mα)
 wp1, ··· , wp(α−1) → wm1,new, ··· , wm(α−1),new
 wm(α+1), ··· , wmM → wm(α+1),new, ··· , wmM,new
 wmα,new = wmα − β(wmα − wpα)
 For parent 2 (pα) → offspring 1 (pα)
 wm1, ··· , wm(α−1) → wp1,new, ··· , wp(α−1),new
 wp(α+1), ··· , wpM → wp(α+1),new, ··· , wpM,new
 wpα,new = wpα − β(wmα − wpα)
 Mutation
 Mutation allows to change the value of a component of the chromosome. The total
number of variables that can be mutated equals to ceiling of the mutation rate times
the population size. The row and column numbers of variables are nominated randomly
and then these nominated variables are replaced by new random ones.
 The GA-based optimization algorithm for SDF-based CSS is illustrated on the flowchart
Fig. 4.
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 Tạp chí Khoa học và Kỹ thuật - Học viện KTQS - Số 184 (06-2017)
 Table 1. Simulation GA parameter setup
 Parameter Value
 Population Size 30
 Number of Generations 500
 Elitism 1
 Crossover Probability 95%
 Mutation Probability 30%
 Initialization Method Random
 Crossover operation Single point
 Selection Method Roulette wheel
4. Experimental Results
 In this section, the proposed method is implemented on Matlab and its results are
compared with the other methods. In the following experiments, the GA parameters are
set as in table 1.
4.1. Single SU vs. Multiple SU with GA
 In Fig. 5, we plot the receiver operating characteristics (ROC) curve for various num-
bers of cooperative CRs and different channel conditions. CR and channel parameters
base on work presented in [9]: the transmitted signal s[n] = 1, number of samples
N = 20, the variance of the AWGN of PU-SU links and AWGN of SU-FC links is set
to 1. We evaluate the performance of GA for CSS in 3 scenario as follows
 • Scenario 1: Number of CRs M = 6, the local average SNR of a single sample at
 individual SUs are [−3.7, −5.2, −3.4, −5.4, −9.5, −3.8] in dB.
 • Scenario 2: Number of SUs M = 3, the 3 SUs with highest SNR values among
 those 6 SUs in scenario 1 are chosen.
 • Scenario 3: Number of SUs M = 1, the SU with highest SNR values among those
 6 SUs in scenario 1 are chosen.
 For each scenario, the channel gains and sensing noises that eventually influence the
detection performance are randomly generated. As shown in Fig. 5, it is clear that when
number of SU is 1, the result of the proposed method is very close to conventional ED
based spectrum sensing with single SU. The results also demonstrated that the more
SUs in CRN being used for CSS, the better the performance of spectrum sensing, this
result is also kept even the SNR of the additional SUs is pretty small.
4.2. Multiple SU with GA vs. Multiple SU with GA and Noise Power Estimation
 The results in [9] showed that detection performance is more sensitive to the sensing
noise change (PU–SU links) than that of the communication channel noise (SU-FC
links). In addition, spectrum sensing period should be as short as possible. Therefore,
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Chuyên san Công nghệ thông tin và Truyền thông - Số 10 (06-2017)
 Fig. 4. Flowchart of GA-CSS
within this proposal, we only use EM algorithm for noise power estimation of PU–SU
links. It is noted that noise power estimation is only needed to perform once per several
sensing period. The simulation parameters are set as follows: number of PUs, M = 6,
other parameters as in subsection 4-A. In this simulation, 2 cases of noise power changes
are considered.
 • Case 1: the true noise power is higher than the predefined one.
 • Case 2: the true noise power is lower than the predefined one.
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 Tạp chí Khoa học và Kỹ thuật - Học viện KTQS - Số 184 (06-2017)
 Fig. 5. ROC curve for different number of SUs
 Fig. 6 shows that if true noise power value, which can be estimated by EM, is
lower than predefined values as in subsection 4-A then EM-GA based CSS has better
detection performance than GA based CSS. To be specific, as can be seen, given the
false alarm probability Pf = 0.1, our proposed method produces the result Pd ≈ 0.93
while the conventional GA can only gives Pd ≈ 0.86. In contrast, when noise power
is lower than predefined one, EM-GA based CSS is able to point out the accurate
ROC curve compared to GA based CSS. In details, when Pf = 0.1, the correct
misdetection probability is Pmd = 1 − Pd should be approximately 0.22, while the
result of conventional GA without noise power estimation showed that the interference
from SUs to the operation of the PU is only about 0.14 which is not correct. For both 2
cases, our proposed method can provide more reliable information for SUs to utilize the
channel with the constraints of the probability of detection and false alarm probability
compared to conventional GA.
 Since EM must be performed for noise power estimation before GA procedure, it is
needed to examine the performance of the system in the time manner. To do so, the
performance of the EM was evaluated when assuming that 50% of noise measurements
was unobservable. Note that when the percentage of unobservable data increases, EM
will need more iterations to converge. As our experimental results, EM needs about
22 iterations to converge with the elasped time for those iterations is roughly 0.001
seconds. This result must be multiplied with number of SU in the CRN to produce
the total processing time of EM. To compare the processing time of EM and GA, we
also examine the performance of GA and the results show that GA needs about 120
generations to converge with the elasped time of about 0.1 seconds when number of
SU is 6. Due to the obtained results, the GA-EM procedure needs about 6% additional
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Chuyên san Công nghệ thông tin và Truyền thông - Số 10 (06-2017)
 Fig. 6. Comparing between proposed method (EM-GA) and conventional GA
time compared to the GA procedure only. However, it has to be noted that, if the system
hardware supports pipeline procedure, i.e., Field Progammable Gate Array (FPGA), EM
procedure will obviously not affect the system performance.
4.3. Performance of GA
 To evalutate the performance of GA, the maximum fitness and mean of fitness are
examined as shown in Fig. 7. It is noted that the parameter of GA and CRN are as
the same as the previous experiments and, without loss of generality, the noise power
is assumed to be the predefined one. As can be seen, GA converges after about 120
generations and the Pd after convergence is consistent with the results shown in Fig. 6.
5. Conclusions
 In this paper, a GA-based optimization of weighted CSS has been evaluated in which
the weights are assigned to the information provided by the SUs to improve CSS in
terms of ROC. To further enhance the performance of CSS, this paper proposes to use
EM algorithm to estimate noise power before employing GA. The simulation results
demonstrated that the proposed method is able to produce more accurate ROC curve
compared to the traditional method which uses predefined noise power values. It must
be noted that ROC curve of a CR system influences its performance in the aspects of
spectrum efficiency or interference to the licensed user.
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 Fig. 7. Performance of GA
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 Manuscript received 13-4-2017; accepted 23-8-2017.
 
 Hoang Manh Kha received the B.E and M.E degree in Electronics-Telecommunications both
 from the Hanoi University of Science and Technology, in 2002 and 2004, respectively, the PhD
 degree in Communications Engineering from University of Paderborn, Germany in 2016. He
 is working as a lecturer at Faculty of Electronics, Hanoi University of Industry. His research
 interests include digital signal processing, wireless communication, positioning engineering.
 Nguyen Viet Tuyen received the B.E degree in Electronics-Telecommunications, the M.E
 degree in Information processing and Communication both from the Hanoi University of
 Science and Technology, in 2001 and 2006, respectively. He is working as a lecturer at
 Faculty of Electronics, Hanoi University of Industry. His research interests include digital
 signal processing, wireless communication.
 Nguyen Hai Duong received the B.E degree in Radio and information communication, the
 M.E degree in Electronic Engineering both from Le Quy Don Technical University, Hanoi,
 Vietnam, in 1995 and 1998, respectively and the PhD degree in Electronic Engineering from the
 Lomonosov Moscow State University in 2007. He is working as a lecturer at Faculty of Radio
 Electronics, Le Quy Don Technical University, Hanoi, Vietnam. His research interests include
 digital circuits and systems, microprocessor engineering, wireless and satellite communication.
 Vo Kim was born in Quang Ngai. He received the B.E degree in Radio and information
 communication from the Hanoi University of Science and Technology in 1967, the PhD degree
 in Electronic Engineering from the Military Academy of Hungary in 1979 and Associate
 Professor in 1991. He is working as a lecturer at Faculty of Radio Electronics, Le Quy Don
 Technical University, Hanoi, Vietnam. His research interests include multi-user communication
 technique, space-time processing technique, wireless and satellite communication.
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