MIMO-enhanced distributed spectrum sensing with diffusion based algorithms for cognitive radio systems

Abstract

Spectrum sensing (SS) is a fundamental function in cognitive radio (CR) networks, enabling efficient spectrum utilization by identifying available channels. However, existing SS methods face challenges such as low accuracy in dynamic and low signal-to-noise ratio (SNR) environments, as well as high computational complexity. To address these issues, this paper presents a distributed SS technique that combines multiple-input multiple-output (MIMO) technology with a diffusion-based (DB) cooperative algorithm. MIMO enhances spatial diversity to improve detection performance, while the DB algorithm enables efficient collaboration among secondary users, reducing both sensing time (ST) and computational time (CT). Simulations over Rayleigh (RL) and Rician (RC) fading channels evaluated metrics such as probability of detection and false alarm. Results demonstrate that the proposed MIMO-DB method outperforms existing approaches, including honey badger remora optimization (HBRO)-AlexNet, by reducing ST by 18 seconds and CT by 45 seconds at 5 dB SNR, while achieving higher detection accuracy across varying SNR levels. These findings highlight the method’s robustness and efficiency, making it a promising solution for dynamic spectrum management in 5G, internet of thing (IoT) and other next-generation wireless systems.