A Collaborative Spectrum Sensing Algorithm Based on Signal-To-Noise Ratio Comparison in Cognitive Radio

Cognitive radio (CR) is pivotal in optimizing spectrum utilization via sensing and sharing. Given the significant impact the signal-to-noise environment has on spectrum sensing performance, this paper introduces a collaborative spectrum sensing scheme centered on signal-to-noise ratio (SNR) comparison, specifically tailored for multi-user scenarios characterized by low SNR. Unlike conventional approaches that involve all cognitive users in spectrum sensing and information fusion, our algorithm selectively engages users whose SNR surpasses the average SNR of the entire cognitive user group. We derive the probability of cooperative spectrum detection under different rules. Numerical simulation demonstrate that our presented scheme consistently outperforms traditional collaborative approaches employing“OR”and“AND”rules regarding detection probability. The presented collaborative spectrum sensing algorithm reduces sensing costs while enhancing detection probability.