Performance Analysis of Cooperative Spectrum Sensing in Cognitive Radio Networks

This study presents a comprehensive simulation-based performance analysis of cooperative spectrum sensing in cognitive radio networks. The implementation utilizes MATLAB simulation tools to model the cooperative spectrum sensing process, incorporating key algorithms such as energy detection, matched filtering, and cyclostationary feature detection. The simulation framework includes centralized and distributed cooperation strategies, where multiple secondary users collaborate to detect primary user signals through decision fusion techniques like AND rule, OR rule, and majority voting. Through extensive simulation experiments, we demonstrate that cooperative spectrum sensing significantly improves spectrum utilization efficiency by reducing missed detection probabilities and false alarm rates. The performance metrics analyzed include probability of detection (Pd), probability of false alarm (Pfa), and receiver operating characteristics (ROC) curves under varying signal-to-noise ratio (SNR) conditions. The study further investigates the impact of different cooperation strategies on system performance, examining factors such as the number of cooperating users, reporting channel quality, and fusion center design. We propose several optimization strategies including adaptive threshold adjustment, user selection algorithms, and weighted decision fusion methods to enhance detection reliability. The simulation code implements key functions for signal generation, channel modeling, detection algorithms, and performance evaluation, providing a modular framework for further research. Overall, this work offers in-depth performance insights into cooperative spectrum sensing and serves as a valuable reference for advancing cognitive radio technology development and practical applications.