Geolocation database assisted sub-Nyquist cooperative spectrum sensing for cognitive radio networks

Cognitive radio technology emerges as a highly promising solution for optimizing spectrum utilization in next-generation wireless communication systems. Spectrum sensing is crucial for cognitive radio systems, yet implementing Nyquist wideband spectrum sensing poses significant challenges, especially for compact commodity radios with limited energy and computational capabilities. To address these challenges, various sub-Nyquist alternatives have been developed, which overcome the limitations of traditional Nyquist sampling. However, sub-Nyquist techniques, which rely on compressive measurements, are vulnerable to issues such as multipath fading, shadowing, noise uncertainty, and varying channel conditions. To mitigate these issues, a cooperative sensing technique is employed, leveraging the spectral diversity of secondary users. This method enhances detection performance by having secondary users share the recovered spectral support with the master device. The master device then collectively determines the spectrum occupancy status, using the aggregated data to ensure optimal use of available frequencies and reducing the likelihood of interference with licensed users. To further streamline the process and improve detection performance, integrating prior information from a geolocation database is considered, resulting in a hybrid approach. This paper introduces a novel hybrid sub-Nyquist cooperative wideband spectrum sensing technique designed for cognitive radio networks. The primary objectives of this technique include reducing computational and implementation complexity, particularly compared to conventional spectrum sensing schemes. Simulation results validate the efficacy of the proposed hybrid scheme, demonstrating superior detection performance compared to cooperative as well as non-cooperative sensing schemes. This research marks a significant advancement in addressing the challenges of spectrum sensing in cognitive radio networks, offering a more efficient and robust solution for spectrum utilization in dynamic wireless environments.