Comparative Analysis of Blind Detectors in a Cluster-Based Cooperative Spectrum Hole Detection
Keywords:
Eigenvalue Detector (EVD), Energy Detector, Cyclostationary Detector, Cognitive User (CU), Spectrum Hole (SH) and Cluster.Abstract
Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate.
References
Abbass, N.; H. H. Hussein; A. C. Jad; M. Ali and Y. Koffi-Clément. (2021). Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge, Sensor. MDPI, 5 :1-29. doi.org/10.3390/s21072408
Abolade, R.O; S.I Ojo; I.A Ojerinde; J.S Adetunji and A.T. Lawal. (2020). Modification of Maximal Ratio Combining Technique for Detection of Spectrum Hole in a Cognitive Radio Network. International Journal of Wireless and Microwave Technologies, 7(2): 9-21. doi:10.5815/ijwmt.2020.02.02
Adeyemo, Z.K.; S.I. Ojo; R.O. Abolade and O.B. Oladimeji. (2019). Modification of a Square-Law Combiner for Detection in a Cognitive Radio Network. International Journal of Wireless and Microwave Technologies, 4(2): 32-45. doi10.5815/ijwmt.2019.02.04.
Arijit, N. and Nityananda, S. (2017). A distributed solution for cooperative spectrum sensing scheduling in multiband cognitive radio networks, Journal of Network and Computer Applications, 94(3): 67-76.
Chhagan, C. and Rajoo, P. (2019). Cooperative Spectrum Sensing Using Eigenvalue-Based Double-Threshold Detection Scheme for Cognitive Radio Networks, Journal of Advances in Intelligent Systems and Computing 13(8): 189-194.
Dong, X. J.; Y.B. Chen; G.Y. Yang; X.S. Pang and J.X. Yang. (2015). The optimization of improved energy detector in Cognitive Radio network. International Conference on Computer Information Systems and Industrial Applications, Yunnan Minzu University, pp 87-93. doi: 10.2991/cisia-15.2015.23.
Gevira, O. O. (2016). Design of an optimal eigen valuebased spectrum sensing algorithm for Cognitive Radio, Master thesis submitted to University of Nairobi, pp 12-47.
Haykin, S. (2005). Cognitive Radio Brain-Empowered Wireless Communications, IEEE Journal on Selected Area in Communication 3(23): 201-210.
Jayanta, M.; K.B. Deepak and K.S. Manoj. (2014). Cyclostationary Based Spectrum Sensing in Cognitive Radio: Windowing Approach. International Journal of Recent Technology and Engineering, 3(1): 95-99. doi:10.1049/ic:20080398.
Jingwen, T.; J. Ming; G. Qinghua and L. Youming. (2018). Cooperative Spectrum Sensing: a blind and Soft Fusion Detector. IEEE Transactions on Wireless Communications, 17(4):27262737. doi: 10.1109/TWC.2018.2801833.
Josip, L.; R. Ivana and B. Dinko. (2022). Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMOOFDM Cognitive Radio Systems. MDPI: Sensors 2022, pp 1-29. doi.org/10.3390/s22020631.
Komal, P. and Tanuja D. (2016). Review on: spectrum sensing in Cognitive Radio using multiple antenna. International Journal of Innovative Science, Engineering and Technology 3(4):313-318. doi:10.1155/2010/381465.
Meenakshi, S.; C. Prakash and S. Nityananda. (2016). A brief review of cooperative spectrum sensing: issues and challenges. IEEE Transaction on Wireless Communication 16(3):1-4. doi:10.1109/ICADW.2016.7942523.
Mohsin, A. and Haewoon, N. (2019). Optimization of spectrum utilization on cooperative spectrum sensing. Journal of sensor network 19(8): 1-13. doi: 10.3390/s19081922.
Nikhil, A. and Rita M. (2017). Cooperative spectrum sensing using hard-soft decision fusion scheme. International Journal of open Information Technologies 5(5):36-39. doi:
1109/IACC.2017.0057.
Noor, S. (2017). Combined soft hard cooperative spectrum sensing in Cognitive Radio networks. Ph.D dissertations submitted to University of Windsor, pp 1-79. doi:
1109/T-WC.2008.070941.
Ojo, S. I.; Z. K. Adeyemo; D. O. Akande and A. O. Fawole. (2021a). Energy-Efficient Cluster-Based Cooperative Spectrum Sensing in a Multiple Antenna Cognitive Radio Network. International Journal of Electrical and Electronic Engineering & Telecommunications, 10(3):176-186. doi:
18178/ijeetc.
Ojo S. I.; Z. K. Adeyemo; F. K. Ojo; A. A. Adedeji and F. A. Semire. (2020). Enhancement of Equal Gain Combiner for Detection of Spectrum Hole in a Cognitive Radio System. Universal Journal of Electrical and Electronic Engineering 7(5):289-298. doi: 10.13189/ujeee.2020.070503.
Ojo, S. I; Z. K. Adeyemo; R. O. Omowaiye and O.O Oyedokun. (2021b). Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network. Indonesian Journal of Electrical Engineering and Informatics 9 (4):834-845. doi: 10.52549/ijeei.v9i4.3179.
Ojo, F.K. and Fagbola, F.A., (2015). 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, 3(6): 58-59.
Pawel, S.; M. Krzysztof and T. Jerzy. (2022). Cognitive Radio Networks for Urban Areas, Building the Electromagnetic Situation Awareness in MANET. MDPI: Sensors 2022, PP 1-19. doi: 10.3390/s22030716.
Runze, W.; D. Lixin; X. Naixue; S. Wanneng and Y. Li (2019). Dynamic Dual Threshold Cooperative Spectrum Sensing for Cognitive Radio Under Noise Power Uncertainty.
International Journal of Computing and Information Science, 22(9): 2-18. doi.org/10.1186/s13673-019-0181-x.
Samrat, C. S. and Ajitsinh, N. J. (2016). Centralized cooperative spectrum sensing with energy detection in Cognitive Radio and optimization. IEEE International Conference on Recent Trends in Electronics Information Communication Technology, India, PP 1002-1006. doi: 10.1109/RTEICT.2016.7807980.
Saeid, S.; T. Abbas and S. Joseph. (2013). Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radio. IEEE Transactions on Wireless Communications, 12(11):57555758. doi: 10.1109/TWC.2013. 100213.130158.
Shraddha, E. J.; E. P. Deepak and E. S. Pawan. (2018). Eigenvalue Detection for Spectrum Sensing in Cognitive Radio Network over Nakagami Fading Channel. International
Journal of Advanced Research in Electronics and Communication Engineering, 7(6): 1-4. doi:
1109/TCOMM.2009.06.070402.
Syed S. A.; L. Chang; L. Jialong; J. Minglu and M. K. Jae. (2016). On the Eigenvalue Based Detection for MultiAntenna Cognitive Radio System. International Journal of Mobile Information Systems 5(3):18.doi.org/10.1155/2016/38 48734.
Yang, L.; Z. Zhangdui; W. Gongpu and H. Dan. (2015). Cyclostationary Detection Based Spectrum Sensing for Cognitive Radio Networks. International Journal of Communications, 10(1):74-8. doi.org/10.1155/2022/7941978.
Yashaswini, S.; S. Ritu and K. K. Sharma. (2022). Optimization of inter fusion rule threshold for energy efficient in a cluster based CSS over Nakagami and Rician fading
channels. International Journal of Electronics: Taylors and Francis, pp 1-6. doi.org/10.1080/00207217.2022.2117853.
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