Error recovery with relaxed MAP estimation for massive MIMO signal detection

This paper proposes a maximum a posteriori (MAP) estimation-based error recovery method for massive multiple-input multiple-output (MIMO) signal detection. The error recovery is a technique to improve the estimate of transmitted signals taking advantage of the sparsity of the error signal. We formulate the error recovery problem as the MAP estimation, where not only the sparsity but also the discreteness of the error are taken into consideration explicitly. In the proposed MAP estimation, we can also use not only the hard decision of the transmitted signal vector but also the soft decision obtained before the error recovery. The problem of MAP estimation is relaxed into the sum-of-absolute-value optimization problem, which can be efficiently solved with proximal splitting methods. Simulation results show that the proposed method outperforms the conventional method in terms of bit error rate (BER) performance.