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

Regularization is a widely employed technique to solve a poorly conditioned linear inverse problem. This paper investigates the use of regularization to detect the sparse active support set of users in a cognitive radio network. The practical case of the users not being synchronized is considered. To carry out the above investigation, the paper presents a compressive sensing (CS) based regularization method for identifying the active subcarriers of the asynchronous multicarrier modulation signals at the sub-Nyquist sampling rate. The proposed approach is the regularized asynchronous multiple measurement vector (MMV) Order Recursive Matching Pursuit (Regularized Asyn-chronous M-ORMP), which is a greedy algorithm. Simulation results suggest that when multiple measurements are considered, the performance becomes highly sensitive to the regularization parameter selection. A simplified algorithm is also proposed that achieves near optimum performance without such sensitivity.