International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.9, No.2, Mar. 2019

Modification of a Square-Law Combiner for Detection in a Cognitive Radio Network

Full Text (PDF, 1107KB), PP.32-45

Views:3   Downloads:0


Zachaeus K. Adeyemo, Samson I. Ojo, Robert O. Abolade, Olusola B. Oladimeji

Index Terms

Square Law Combining;Probability of Detection;Probability of Missing; Primary User;Secondary User;Cognitive Radio;Detector


Spectrum sensing is of paramount importance in the Cognitive Radio Network (CRN) due to massive spread of wireless services. However, spectrum sensing in CRN is affected by multipath effects that make detection difficult. Square- Law Combining (SLC) technique, which is one of the methods previously used to address this problem, is associated with hardware complexity that results in long processing time. Hence, this paper aim to modify SLC technique for primary user detection in the CRN. The modified model consists of three Secondary User (SU) antennas which receive the faded signals through the Rayleigh fading channel. The received signals are combined using Switch Combiner (SC) at Radio Frequency (RF) stage. The selected signal passes through only one Energy Detector (ED) before making decision. The modified model is incorporated into simulation model which consists of Primary User (PU) transmitter that processes the randomly generated data through some signal processing techniques for transmission to the SU receiver. Probability of False Alarm (PFA) expression is derived for the modified Square-Law Combiner (mSLC) to set the thresholds at 6.64 and 9.14 for PFA of 0.01 and 0.02, respectively. The modified model is evaluated using Probability of Missing (PM), Probability of Detection (PD) and Processing Time (PT) to determine the performance. The results of the mSLC show that at SNR of 4 dB and PFA of 0.01, the values obtained for PD, PM, PT are 0.6575, 0.3530, 5.5540 s, respectively, as against the conventional SLC of 0.4000, 0.600, 6.2055 s, respectively. At SNR of 4 dB and PFA of 0.02, the values obtained for the mSLC are 0.7600, 0.3457, 6.1945 s for PD, PM and PT, respectively, as against 0.4000, 0.6000, 7.2197 s for conventional SLC. The results show that mSLC gives lower PM, higher PD and lower PT values when compared with conventional SLC.

Cite This Paper

Zachaeus K. Adeyemo, Samson I. Ojo, Robert O. Abolade, Olusola B. Oladimeji, "Modification of a Square-Law Combiner for Detection in a Cognitive Radio Network", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.9, No.2, pp. 32-45, 2019.DOI: 10.5815/ijwmt.2019.02.04


[1]Mittiades, C.F., David, G. and Geoge, A.R. A Comparative Performance Analysis of Interweaved and Underlay Multi-Antenna Cognitive Radio Networks, Journal of Energy supporting post-doctoral Researchers, 2013. 2(5): 68-70.

[2]Vadivelu, R., Sankaranyanyanan, K. and Vijayakumari, V. Matched Filter Based Spectrum Sensing for Cognitive Radio at Low Signal to Noise Ratio, Journal of Theoretical and Applied Information Technology, 2014. 62(1): 108-111.

[3]Ojo, F.K. and Fagbola, F.A. Spectrum Sharing in Cognitive Radio Work Using Good put Mathematical Model for Perfect Sensing, Zero Interference and Imperfect Sensing Non Zero interference, international Journal of Wireless Communication and Mobile Computing, 2015. 3(6): 58-59. 

[4]Kevin, C. Spectrum Sensing, Detection and Optimization in Cognitive Radio for Non-Stationary Primary User Signals, unpublished Ph.D Thesis submitted to Queensland University of Technology, Network and Communication, Faculty of Science and Engineering, 2012.  pp 12-46.

[5]Kaniezhil, R. and Chandraseker, C. Performance Analysis of Wireless Network with Opportunistic Spectrum Sharing via Cognitive Radio Nodes, journal of Electronic Science and Technology, 2012. 10(4): 99-208. 

[6]Adeyemo, Z.K. and Ojedokun, I.A. EGC Receiver using Single Radio Frequency Chain and Single Matched Filter over Combined Rayleigh and Rician Fading Channels, ARPN Journal of Engineering and Applied Sciences, 2014. 9(7): 992-994.

[7]Refik, F.U. Spectrum Sensing Techniques for Cognitive Radio Systems with Multiple Antennas, unpublished Master thesis in Electronics and communication Engineering, Graduate School of Engineering and Sciences, 2010.  pp 2-10.

[8]Ahmed, B. and Tamer, K. A Hybrid Spectrum Sensing Technique with Multiple Antenna   Based on GRT, IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communication Qatar University, 2013. pp 10-23.

[9]Suman, R., Rajeshwar, L. D. and Parmender, S. Spectrum Sensing in Cognitive Radio Using MIMO Techniques, International Journal of Soft Computing and Engineering, 2011.  1(5): 259-261.

[10]Digham, F., Aloumi M. and Simon M. Energy Detection of Unknown Signals over Fading Channel, IEEE transactions on communications, 2007.  55(1): 21-24.

[11]Hussien A. Performance Analysis of Energy Detection over Different Generalized Wireless Channel Based Spectrum Sensing in Cognitive Radio, Ph.D thesis submitted to Department of Electronic and Computer Engineering, Brunel University, London, United Kingdom, 2015.  pp 10-40.

[12]Goldsmith, A.J. Wireless Communication” first Edition. Cambridge University press. Cambridge, England, 2005.  pp 40-100.

[13]Stuber, G.L. Principle of Mobile Communication, kluwer Academic, New York, 2002.  PP 12-32.

[14]Mitola J. Cognitive Radio an Integrated Agent Architecture for Software Defined Radio, PhD thesis, Royal institute of Technology, 2000.  pp 27-214.

[15]Vadivelu, R., Sankaranyanyanan, K. and Vijayakumari, V. Matched Filter Based Spectrum Sensing for Cognitive Radio at Low Signal to Noise Ratio, Journal of Theoretical and Applied Information Technology 62(1), 2014, pp108-111.

[16]Mansi, S. and Gajanan, B., Spectrum Sensing Techniques in Cognitive Radio Networks, International Journal of Next Generation Networks 3(2), pp 2011 37-40.

[17]Megha, M. A Survey on Data and Decision Fusion Strategies on Spectrum Sensing in   Cognitive Radio Networks, International Journal of Advanced Research in Computer and Communication Engineering 3(7), 2014, pp 7510-7512.

[18]Saeid, S., Abbas, T. and Joseph, S. Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radio, IEEE Transactions on Wireless Communications, 12(11), 2013, pp 5755-5758. 

[19]Komal, P. and Tanuja, D. Review on Spectrum Sensing in Cognitive Radio Using Multiple Antenna, International Journal of Innovative Science Engineering and Technology 3(4), 2016, pp 313-315.

[20]Mahmood, A.A. and Zahir, A.H. A new multiple Antennas Method Based Energy Detector for Cognitive Radio over Fading Channels, International Journal of Computer Applications, 52(5), 2012, pp 20-21.

[21]Yawgeng, A.C. and Mostafa, A. Group Switch and Stay Combining with Branch Partition for Space Diversity, International Journal of Future Computer and Communication, 4(4), 2015, pp 226-228.

[22]Ashsish, p. and Linnartz, J.P. Performance analysis of Primary User detection in a multiple antenna Cognitive Radio, IEEE International Conference on Communication 7, USA, 2007, pp 6486

[23]Taruna, T. and Bhumika P. Multiple Detectors Based Analytical Performance of Spectrum Sensing, International Journal of Advanced Computer Research, 4(1), 2014, pp 95-98.