Compressed sensing in spatial MIMO channels

Abstract

Many wireless channels exhibit sparse multipath feature in practice. In this paper, we analyze the sparsity of sparse MIMO channel and the leakage effect with fixed Fourier basis in the spatial/angular domain. In order to enhance the sparsity of the MIMO angular channels we propose an optimized overcomplete Fourier basis dictionary, which is obtained by a sparsity criterion, to represent the signals with the best basis. By converting the compressed sensing from multiple measurement vectors to a single measurement vector, the reconstruction of the MIMO channel is simplified and makes better use of the sparsity of the MIMO angular channels. Simulations show that with the optimized basis dictionary the leakage effect is reduced and the orthogonal matching pursuit algorithm can reconstruct the MIMO channel effectively with the optimized Fourier basis.