Low Complexity Detection Algorithms in Large-Scale MIMO Systems

In this contribution, we present low-complexity detection algorithms in large-scale MIMO systems where they achieve significantly better bit error rate (BER) performance than known heuristic algorithms in large-scale MIMO literature, such as local ascent search (LAS) and reactive tabu search (RTS) algorithms, especially at higher-order modulations. The proposed techniques are developed from the conventional quadratic programming (QP) detector. The first one is based on performing two stages of a QP detector with a novel combination of both interference cancellation and shadow area constraints of the constellation. The second one is based on the branch and bound search tree algorithm. The efficacy of the proposed algorithms is investigated at various QAM modulations. Computer simulations show that the proposed algorithms outperform LAS and RTS algorithms in both uncoded and turbo coded BER performance, especially at higher QAM levels, with no significant change in complexity as the modulation level increases. Also, an extension of the QP detector for iterative detection and decoding is developed for the case of QPSK using a low complexity approach.