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Expectation-propagation based low-complexity channel estimation for massive MIMO systems

In this paper we propose a low-computational-complexity channel estimation algorithm for the downlink of massive MIMO systems. This algorithm employs the expectation propagation and expectation maximization techniques, assuming a sparsity promoting conditional Gaussian prior distribution on the channels. Its computational complexity scales only linearly with the number of transmit antennas, the number of pilots, and the channel length. Compared with existing state-of-the-art algorithms, it has a lower pilot overhead and is much more computationally efficient. The channel estimation mean-squared errors with the proposed algorithm and the existing block iterative support detection (block-ISD) and basic pursuit (BP) methods are simulated and compared. Results show that the performance of the proposed algorithm approaches the benchmark performance, and is significantly better than that of block-ISD and BP.