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Sparsity adaptive channel estimation based on compressed sensing for OFDM systems

Abstract A sparsity adaptive channel estimation is based on compressive sensing for OFDM systems. The proposed method is, namely, regularized sparsity adaptive matching pursuit (RSAMP) that does not need a priori knowledge of the channel sparsity compared with conventional compressed sensing algorithms, and searches the position of maximal difference to choose the supported elements, then regularizes these elements to improve the accuracy of supported set. Simulation results demonstrate that the RSAMP scheme has excellent performance and low computational complexity.