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Abstract

To cope up with the demand of user’s traffic and availability of spectrum of wireless network, cognitive radio can pave the way to optimize them. Cognitive radio networks (CRN) deals with two types of users: one is primary users (PUs) or licensed users who have the higher priority to use a specific part of the spectrum and the other one is secondary users (SUs) or unlicensed users who have less priority of using spectrum. SUs use the spectrum in such a way that they do not cause interference with PUs. In cognitive radio network, different techniques (i.e. Energy detection, Cyclostationary Feature Detection, Matched filter, Cooperative Sensing, Waveform Based Sensing etc.).In this paper we have studied cooperative sensing energy detection method of cognitive radio network and show the relation of bit error and signal to noise ratio (SNR) in Rayleigh fading channel.

1.1 Introduction:

Cognitive radio (CR), which is the next generation wireless communication system and it enables in wireless communication to unlicensed users or secondary users (SUs) to exploit under-utilized spectrum (called white spaces)[13].The licensed users or primary users (PUs) so that bandwidth availability improves at the SUs, which helps to improve overall spectrum utilization. The number of users in radio spectrum services are communications and radio are increasing day by day and it is the most important for economic growth and many social activities and developing work. This is becoming obviously great effort in the wireless and mobile communication. At the same time spectrum sensing is facing some problem. So, the issue of spectrum sensing in wireless communication can be solved in a better way using cognitive Radio (CR) technology [14][17]. These have been used for improving the performance of spectrum sensing. This is used for better performance against the network. In this network, there are different types of malicious users such as greedy users, unintentionally misbehaving users. The performance of spectrum sensing in the network i.e. minimization of false alarm and maximization of detection. These two parameters are heavily affected by the presence of malicious users inside the network. Cognitive Radio is an adaptive intelligent radio and network technology that can automatically detect available channels in a wireless communication [1] - [3].

So many researcher to submit to change the technology and especially almost in wireless communication and other part of job. In the several work submitted, several researches stated that both of all time and frequency much have licensed spectrum unused. On the other hand traffic in wireless network tends to be burst. Spectrum sensing depends on the ability to use the unused spectrum. The spectrum sensing is a key functional factor in cognitive radio. And energy detector (ED) is a one of the best spectrum sensing technique that does not require prior information about the primary signal. This technique is very simple and expense if its performance at low SNR.But there are some problems with spectrum sensing in CR. Because maximum research focuses on spectrum sensing in CR. So, in theoretically detection algorithms not enough which the process of cognitive radio of spectrum sensing [15]

    

                          

  Fig 1: Spectrum Band [6]                          Fig 2: Spectrum white space [7]

Results:

Optimization of Cooperative Spectrum sensing to minimize the total error rate:

 

         

From figure 3, shows the total error probability versus the threshold for SNR=10dB and n=5 using ED technique.

Here local spectrum sensing technique is ED and the local SNR=10dB and N=20 samples are used for this spectrum sensing. From figure 4 shown the threshold vs. total error rate using ED technique [6].

From figure 4, shows the total error probability versus threshold for different number of n=1, 2, 3, 4 ….k and out of CRs that controls the fusion rule using ED technique. If we compare the different curves that represent the total error for different number of n in figure 3. We observe there are difference in the performance through using n=1 to 10 and as an n=10 fusion rule. Here n=10 which represent ‘AND’ fusion rule and give high total error rate compared to the other curves; it is found that the minimum total error in n=5 are the same value of SNR and threshold. [4]

In this figure, we get the optimum value of ‘n’ out of ‘K’ CRs. We vary threshold value from 10 to 40 and for different SNR values (0dB, 5dB, 10dB), we found optimal value of ‘n’ from optimal voting rule. From graph we conclude that for low threshold value with low SNR, the required number of CR’s is more. We increase threshold value with low or same SNR then we requires very less number of CR. And also SNR increases the optimal value of n increases. E.g. If SNR=0dB and threshold=33 then optimal value of n is 1.That is with 1 CR we can achieve low error rate [8][6].

For high threshold value, optimal value of n is small, so for high threshold value with number of CR’s, we get probability of missed detection false alarm probability. Also the probability reduces by decreasing SNR value for small number of ‘n’ in an AWGN channel. As usual we also seen that.

 

    

Table 5.1: Optimal number of ‘n’ CRs for different SNR and its error level

No SNR in dB Error level (Minimum) Number of Cognitive Radio User

1 5 '〖'10'〗'^(-0.6) 4 or 5

2 10 '〖'10'〗'^(-2.6) 5

3 13 '〖'10'〗'^(-6.4) 5 or 6

4 15 '〖'10'〗'^(-11) 6

Table 1 shows the optimal fusion rule and minimum error when SNR is varied and the ED is used with number of samples (i.e. N=20).The improvement in the performance by increasing the total number for different SNR at CRs at fixed N .For example min error=0.2511 when SNR=5dB and CRs=4 or 5.The increase in SNR causes decrease the minimum error and variation in CRs. We also find out the value of different SNR=17, 18 or 20 for user of cognitive radio is increased. [5][16]

6.2. Energy Detection Simulation:

It is the energy detection performed over a Rayleigh channel exhibits a tough detection performance.

From figure 7, we get the concept of energy detection of Rayleigh channel is improved performance achieved by less number of samples (L).

1) It is increased the false alarm depend on the SNR value.

2) If SNR value is increased so false alarm is increased.

3) On the other hand when SNR value is less so false alarm is decreased. Energy signal increases for a given number of samples L.

4) False alarm is depending on the SNR value. When SNR value is increased then minimum error level is '〖'10'〗'^5 that Rayleigh channel become low now of user of cognitive radio.

5)This  is the complementary  ROC  over  Rayleigh  fading channel  for average  SNR  values of 20-35 dB and time bandwidth product  different for different sample u=L/2 and sample size L=10-25 is as shown in Fig 9.

6) From P_M -P_FA plot, it is observed that the slopes are low for P_M<0.1 and 10dB increase in SNR (from 15dB to 25dB) has a decreases in missed detection probability (reducedP_D).

7) It is apparent that energy detection executed over a Rayleigh channel exhibits a tough detection performance [9-10].

From figure 7, it is shows the changing SNR value for changing curve. Different SNR for different curve in different samples of Here SNR value is 20dB , 25dB, 30 dB, 35 dB and sample value is L=10,15,20,25.

In this figure see that different SNR for change the probability of miss detection (P_M) and probability of false alarm '〖'(P'〗'_FA) is constant.[11]

                                                                                                             

From fig 8, decreasing threshold value in same time changing also SNR value. In the last figure 6.2(a) seen that the yellow curve is decreasing for changing the threshold point. In this figure we see that the threshold range is much decreasing for SNR=35dB then yellow curves not shown in there.

From Figure 9, we see that the figure shows the decreasing the value of P_FA from P_M and we should change the value of threshold for decreasing theP_M. Here show the how can change the curve in decreasing the value of threshold. [12].

Conclusion:

In this paper we are discussed about spectrum sensing based on energy detection in CR networks. This paper contributes to the cooperative spectrum sensing optimizing and energy detection simulation introducing by number of cognitive user and SNR.Here, we may have different SNR depend on the total error rate and probability of false alarm and detection. As per CR module changing of SNR value, we get different total error rate For SNR value 5,10,13,15 we get number of cognitive of cognitive radio user 4or 5, 5, 5 or 6, 6.On the other hand, ROC curves used to plots of the probability of detection vs. the probability of false alarm. The probability of detection varies based on SNR and false alarm probability various time bandwidth factors. When SNR increases the detection probability increases and get SNR=25dB is better where detection probability. And detection probability depend on time bandwidth factor. If time bandwidth factor increases, the detection probability decreases. And false alarm increases, the detection probability increases. So we almost get the final result of spectrum sensing for cognitive radio based on Energy Detection as we expected.

References:

[1] I. Mitola, J. Maguire, G. Q., “Cognitive radio: making software radios more personal.” Personal Communications, IEEE. vol . 6, no. 04, pp. 13-18, Aug 1999.

[2] R. Chen, JM park J Reed, “Defense against primary user emulation attacks in cognitive radio networks.” IEEE J. Selected Areas Commun. vol. 26, pp. 25-37, 2008.

[3] S. D. Tanuja and S. Dina, \"Spectrum Sensing Algorithm for Cognitive Radio Networks for

Dynamic Spectrum Access for IEEE 802.11 af standard,\" International Journal of Research and Reviews in Wireless Sensor Networks (IJRRWSN), vol. 2, pp. 77 - 84, March 2012.

[4]. O. A. Alghamdi, M. A. Abu-Rgheff, and M. Z. Ahmed, \"MTM Parameters Optimization for 64-FFT Cognitive Radio Spectrum Sensing using Monte Carlo Simulation,\" in EMERGING 2010 : The Second International Conference on Emerging Network Intelligence, Florence-Italy, 2010, pp. 107113.

[5]O. A. Alghamdi, M. Z. Ahmed, and M. A. Abu-Rgheff, \"Probabilities of Detection and False Alarm in Multitaper Based Spectrum Sensing for Cognitive Radio Systems in AWGN,\" in The IEEE International Conference on Communication Systems (IEEE ICCS 2010) Singapore: IEEE,

2010

[6] A Ghasemi, ES Sousa,“Spectrum sensing in cognitive radio networks: Requirements, challenges and design trad-offs.” IEEE Commun Mag. vol. 46, no.4, pp. 32-39, 2008.

[7] SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORKS: A SURVEY Mansi Subhedar and Gajanan Birajdar, International Journal of Next-Generation Networks (IJNGN) Vol.3, No.2, June 2011

[8] Gaurav G. Bhosale 1, Dipak B. Khandgaonkar  2and J. Christopher Clement  Student, School of Electronics Engineering, VIT University, Vellore - 632014, TamilNadu, India1Student, School of Electronics Engineering, VIT University, Vellore - 632014, TamilNadu, India2 Professor, School of Electronics Engineering, VIT University, Vellore - 632014, TamilNadu, India3”Cognitive Radio Networks: Optimization of Cooperative Spectrum Sensing to minimize the total error rate.

[9] J Ma, GY Li, BH Juang, “Signal processing in cognitive radio.” Proc IEEE, vol. 97, no. 5, pp. 805-823, 2010.

[10] P. Zhang,” In the development of wireless cognitive science”, Chin. Sci. Bull. vol. 57, pp. 3661-3661, 2012

[11] Lu Lu, Xiangwei Zhou, UzomaOnunkwo and Geoffrey Ye Li, “Ten years of research in spectrum sensing and sharing in cognitive radio.” EURASIP Journal on Wireless Communications and Networking, vol. 48 pp 1-16, 2012.

[12] Hanwu, Zebing Feng, Zhiqin Wei, Zhiyong Feng and Ping Zhang, “Security management based on trust determination in cognitive radio networks.” EURASIP Journal on Advances in Signal Processing, vol. 28, pp 1-16, 2014.

[13] Cognitive Radio Network Architecture and Security Issues by Triparna Mukherjee & Asoke Nath.

[14]Weifang Wang (2009), “Spectrum Sensing for Cognitive Radio’’, Third International Symposium  on Intelligent Information Technology Application Workshops, pp: 410-412.

[15]. D. Cabric, S. M. Mishra, and R. W. Brodersen, \"Implementation issues in spectrum sensing for cognitive radios,\" in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, 2004, pp. 772-776 Vol.1.

[16]Z. Wei, R. K. Mallik, and K. Ben Letaief, \"Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks,\" in Communications, 2008. ICC \'08. IEEE International Conference on, 2008, pp. 3411-3415.

[17]V. Stoianovici, V. Popescu, M. Murroni (2008), “A Survey on spectrum sensi cognitive radio” Bulletin of the Transilvania University of Bra sov, Vol. 15 (50).

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