Efficient iterative soft detection based on polynomial approximation for massive MIMO

In massive multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) detection is near-optimal but involves large dimensional matrix inversion, which results in high complexity. To this end, Neumann series expansion (NSE) approximation, which avoids the direct computation of the matrix inversion, is recently investigated due to its low implementation complexity. Unfortunately, the complexity reduction can only be achieved well when the required number of the NSE terms L is small. To solve this problem, we proposed an iterative NSE (INSE) algorithm for MMSE detection at a manageable complexity even for large L. An approximation method based on NSE is proposed to compute the log-likelihood ratios (LLRs) for channel decoders. Both analytical and numerical results have demonstrated that, the overall complexity of the proposed soft-output MMSE-INSE algorithm is significantly reduced compared with the conventional NSE method and the Cholesky decomposition method, while keeping similar detection performance.