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In this paper, we propose a novel channel estimation algorithm based on theLeast Square Estimation (LSE) and Sparse Message Passing algorithm (SMP), whichis of special interest for Millimeter Wave (mmWave) systems, since thisalgorithm can leverage the inherent sparseness of the mmWave channel. Ourproposed algorithm will iteratively detect exact the location and the value ofnon-zero entries of sparse channel vector without its prior knowledge ofdistribution. The SMP is used to detect exact the location of non-zero entriesof the channel vector, while the LSE is used for estimating its value at eachiteration. Then, the analysis of the Cramer-Rao Lower Bound (CRLB) of ourproposed algorithm is given. Numerical experiments show that our proposedalgorithm has much better performance than the existing sparse estimators (e.g.LASSO), especially when mmWave systems have massive antennas at both thetransmitters and receivers. In addition, we also find that our proposedalgorithm converges to the CRLB of the genie-aided estimation of sparsechannels in just a few turbo iterations.

A LSE and Sparse Message Passing-Based Channel Estimation for mmWave MIMO Systems