Abstract:
In this paper, the compressed sensing theory is used to estimate the channel according to the sparseness of OFDM system channel. Meanwhile, the time selectivity and frequency selectivity of the channel are investigated and a general channel coefficient base spreading model is given. In many practical scenarios, the non-zero components of sparse signals tend to appear in clusters and some more general forms of structured sparsity naturally, e.g., when dealing with the multi-band signals in the measurements of gene expression levels, or in magnetoencephalography. In order to exploit this structure to improve the reconstruction quality, this paper introduces the methodology of group sparse compressed sensing (GSCS). Moreover, by utilizing the sparse representation of channel coefficients and combining the intrinsic group sparse characteristics of the channel, this paper presents an improved optimization method based on GSCS to improve the sparse estimation. This method can correct the wrong atom with a corrective process such that the signal reconstruction performance of the group sparse estimation method can be improved. Finally, the proposed method is applied to a MIMO system, whose simulation results show that it has a certain performance improvement.