The Method of Achieving Broadband Spectrum Sensing on Distributed Compressed Sensing

The first step of spectrum sensing is to collect radio signals for analysis. However, increasingly high sampling rates become the difficulty in broadband spectrum sensing studies. In actual communication, the wide band occupied by primary users possesses sparse features in frequency domain, which conforms to the prerequisite of the Compressed Sensing Theory. Therefore, the work proposes a weighted algorithm for broadband spectrum sensing based on Differential Signal Distributed Compressed Sensing (DS_DCS) by utilizing distributed compressed sensing to achieve broadband spectrum sensing. This algorithm aims to solve the problem of high sampling rate in broadband spectrum sampling. It used Compressed Sensing Technology to reduce sampling rate and differential processing techniques to lower algorithm complexity. To circumvent problems, such as deep fading, hidden terminals and poor noise immunity caused by single node detection, it applied distributed compressed sensing system to conduct cooperative multi-node detection and estimated SNR to weigh signals. Corroborating simulation results show that this algorithm can effectively reduce sampling rates at each node, substantially increase system detection probability and saliently improve system robustness against noise.