Enhancing Cognitive Radio Network Performance through Channel Selection Algorithm

In response to the historical challenge of allocation of spectrum causing scarcity in wireless networks, cognitive radio network technology has evolved as a solution It allows secondary users (SUs) to have dynamic spectrum access by requiring significant modifications to Medium Access Control (MAC) layer operations. In particular, these modifications are related to spectrum sensing and channel assignment, since MAC protocols are essential for arranging sensing intervals and reducing interference. In addressing the imperative for enhanced spectrum sensing accuracy, this research primarily focuses on the productive channel selection algorithm. This algorithm categorizes channels based on descreasing idling probabilities, facilitating swift channel discovery and concurrent sensing. This approach effectively reduces latency and maximizes throughput for SUs. The proposed Extended Generalised Predictive Channel Selection Algorithm has been demonstrated to outperform its predecessor, the Generalised Predictive Channel Selection Algorithm, as showcased through comprehensive Matlab simulations and results visualization.