Low Complexity Lattice Reduction Aided Detectors for High Load Massive MIMO Systems

In this paper, a very low complexity Lattice Reduction technique, called Dual Shortest Longest Vector algorithm (SLV), is adopted to improve the Bit Error Rate (BER) performance of the Minimum Mean Square Error (MMSE) detector in high-load Massive MIMO systems, whereby resulting in the so-called SLV-aided MMSE (MMSE–SLV) detector. An efficient combination scheme of Generalized Group Detection (GGD) algorithm and the MMSE–SLV, called MMSE–GGD–SLV, is further proposed to enhance BER performance of the system more significantly. In order to do so, we first convert the Group Detection approach to the generalized one (GGD) by creating an arbitrary number of sub-systems. Then, an additional operation, i.e., channel matrix sorting, is applied to the GGD to reduce the error propagation between sub-systems. To make the detection complexities of the MMSE–GGD–SLV detector more practical, the MMSE–SLV detection procedure is only applied to the first sub-system. Various BER performance simulations and complexity analysis show that both the MMSE–GGD–SLV and the MMSE–SLV detectors noticeably outperform their conventional MMSE counterpart, yet at the cost of higher detection complexities. However, their complexities are kept at acceptable levels, which are much lower than those of the conventional BLAST detector. Therefore, the proposed detectors are very good candidates for signal recovery in high load Massive MIMO systems. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.