Wide-Band Cooperative Compressive Spectrum Sensing for Cognitive Radio Systems Using Distributed Sensing Matrix

In this paper, cooperative compressive spectrum sensing is considered to enable accurate sensing of the wide-band spectrum. The proposed algorithm is based on compressive sensing theory and aims to reduce the hardware complexity of the cognitive radio receiver by distributing the sensing work among groups of sensing nodes. The proposed algorithm classifies the cooperated sensing nodes into different sensing groups depending on the quality of the reporting channel between the sensing node and the fusion center (FC). To sense the wide-band analog signal and take a global decision about spectrum occupancy, each node uses its local sensing matrix, which is assigned to its sensing group and a part of a global sensing matrix at the FC. The size of the local sensing matrix of each sensing node, and consequently the contribution of this node in the overall measurement vector, depends on its sensing group. The FC classifies and rearranges the compressed data to formulate one global measurement vector which is used with a global sensing matrix to estimate the wide-band signal spectrum. The receiver operation characteristics (ROC) of the overall spectrum sensing system show that the proposed receiver provides more protection to primary users (higher detection probability) at the same secondary user throughput (probability of false alarm).