A practical subspace multiple measurement vectors algorithm for cooperative spectrum sensing

Abstract

Cooperative spectrum sensing (CSS) in cognitive radio networks conducts cooperation among sensing users to jointly sense the sparse spectrum and utilize available spectrums. Greedy multiple measurement vectors (MMVs) algorithm in the context of compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and most of greedy algorithms for MMVs lack for a (robust) stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds for greedy MMVs algorithms without depending on prior information such as sparsity. Moreover, we introduce a practical subspace MMVs greedy algorithm that extends from a subspace-based sparse recovery method to a more practical setting, in which no prior information are required. Extensive simulations confirm the feasibility of the proposed stopping criteria and our sparse recovery algorithm.