Reinforcement learning for performance improvement in cooperative spectrum sensing

Abstract

Cognitive radio (CR) can provide a promising solution to the spectrum scarcity issue for upcoming wireless communication technologies. Cooperative spectrum sensing (CSS) is generally adopted to improve spectrum utilization and minimize interference to primary users (PUs). The performance of CSS is significantly affected by imperfect reporting channels, and it is an easy target for Byzantine attackers. This paper studies CSS under imperfect reporting channels and Byzantine attacks. We have considered centralized CSS with a hard combining fusion rule. The binary symmetric channel (BSC) is used to model the imperfect reporting channels, and a centralized independent probabilistic small scale attack model is chosen to model Byzantine attackers. We first analyze the traditional CSS (T-CSS) under the imperfect reporting channel and the Byzantine attack. The performance of T-CSS is found to be greatly affected in the considered scenarios. We propose a reinforcement learning-based algorithm to detect cooperating secondary users (CSUs) experiencing weak reporting channels and Byzantine attackers. Generally, in literature, the detected malicious users (MUs) are removed to improve the performance. However, in case there are CSUs with weak reporting channels, the genuine CSUs may be detected as Byzantine attackers and hence removed. To overcome this issue, we propose a weighted sum-based CSS (WS-CSS) algorithm that can improve the CSS performance under weak reporting channels and attacks from Byzantine attackers. It is demonstrated using plots that the proposed WS-CSS algorithm significantly improves the CSS performance.