Distributed Streaming Compressive Spectrum Sensing for Wide-Band Cognitive Radio Networks

This paper presents a novel Distributed Streaming Compressive Spectrum Sensing (DSCSS) algorithm for wide-band spectrum sensing under decentralized cognitive radio network (CRN) scenario. In contrary to traditional compressive sensing (CS) that only focuses on fixed-length signal's compressive sampling and reconstruction, DSCSS follows streaming CS framework, where Analog-to-Information Converter (AIC) is utilized to perform streaming signal acquisition below Nyquist sampling rate at individual cognitive radios (CR). Since the sparsity of wide-band spectrum is unavailable in practical situation, DSCSS alternatively estimates the sparsity and the true support set of the spectrum, and the estimated support set is marked and exchanged to the other CRs as a priori information, which are merged and utilized to obtain cooperative sensing gain. This process repeats to acquire performance promotion progressively until robust spectrum sensing results are achieved. Moreover, the low computational complexity makes DSCSS more suitable for on-line applications. Various simulations and comparisons are performed to show the efficiency of the proposed approach, the effectiveness of which is testified.