K-means Clustering for Cluster-Based Cooperative Spectrum Sensing under Rayleigh Fading in Low SNR Conditions
Abstract
The paper discloses the analysis of K-means clustering based cooperative spectrum sensing using energy-based detector for Rayleigh fading channel under low SNR (0 dB to -25 dB) using ROC curves with different rules for fusion “OR-OR, OR-AND, AND-OR and AND-AND fusion rules”. As the current communication systems such as IEEE 802.22 WRAN need to operate with better performance parameters in low SNR conditions, therefore our proposed system considers the similar practical environment. In the proposed system, K-means clustering is used to cluster the secondary users with secondary user closest to centroid selected as cluster head. The clustering technique has provided 70-85% of data overhead reduction in comparison to cooperative or collaborative spectrum sensing. The probability-of-detection doubles in comparison to non-cooperative spectrum sensing at 0.1 probability-of-false-alarm. Further, the comparative analysis is done with other clustering techniques (DBSCAN and LEACH) in terms of data overhead, latency and probability-of-detection. The proposed system has provided an advantage of reduced data overhead, deterministic cluster selection and better performance in terms of detection with OR-AND rule-based fusion in low SNR conditions in comparison to other clustering techniques. Further, the performance variation is also analysed with respect to number of clusters and it has been observed that the detection efficacy of OR-OR and AND-AND rule-based fusion remains unaffected by the variation in number of clusters. However, the detection capability of OR-AND/AND-OR rule-based fusion decreases/increases with the increase in clusters. Further, the OR-AND rule-based fusion performed better than AND-OR rule-based fusion till a certain number of clusters in the network and if clusters increase further, AND-OR rule provided superior performance compared to OR-AND rule. All the simulations were conducted in MATLAB 2024b using TCP-IPv6 model assumptions.References
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