Multi-antenna Compressed Wideband Spectrum Sensing for Cognitive Radio

This paper proposes a novel wideband spectrum sensing (WSS) scheme, termed multi-antenna compressed wideband spectrum sensing (MCWSS) scheme, which utilizes compressed sensing (CS) to reduce the extremely high sampling rate of wideband signal. Although there are studies on compressed wideband spectrum sensing, they only focus on single antenna signal. Since multi-antenna technology can enhance the detection performance, this paper investigates the multi-antenna scenario. However, existing CS recovery algorithms are designed only for single antenna signal and are not suitable for recovering multi-antenna signals. Therefore, the paper proposes two novel CS recovery algorithms from different angles, namely CRL2 (combining relevance via L-2 norm) algorithm and CBS (combining before sampling) algorithm. The CRL2 algorithm jointly recovers the multi-antenna signals and performs better than single antenna scenario. Whereas, CBS algorithm can significantly improves the recovery performance with an additional analog combining operation. Since existing WSS algorithms are too complicated, we devise a novel WSS algorithm, i.e. DA (divided-averaged) algorithm, which has good performance with low complexity. Simulation results show that the MCWSS scheme performs well at low sampling rate.