Joint Sparse Support Recovery for Asynchronous Multicarrier Modulation Signals in Cognitive Radio Networks

Despite the extensive prior work on detecting the sparse active subcarrier support in a cognitive radio (CR) network using a sub-Nyquist receiver, the practical case of the users not being synchronized has not been addressed. This paper presents two compressive sensing (CS) approaches for identifying the active subcarriers of asynchronous multicarrier modulation (MCM) signals at the sub-Nyquist sampling rate. The proposed approaches are the asynchronous multiple measurement vectors (MMV) Orthogonal Matching Pursuit (Asynchronous M-OMP), which is a greedy algorithm, and the subspace based asynchronous MUltiple SIgnal Classification (Asynchronous MUSIC). To implement these algorithms, we first formulate a signal model to establish a relationship between the transmitted symbols and the received signal in an asynchronous transmission environment. As the timing offsets among the asynchronous users are unknown in practice, we also present an approach to estimate them based on sub-Nyquist samples. Various simulation results are presented in this paper to discuss the performance of the above mentioned algorithms in identifying the sparse active subcarrier support.