Enhancing Performance of Massive MU-MIMO System with LR-RTS: A Low-Complexity Detection Algorithm

In recent years, the deployment of massive multiuser multiple-input multiple-output (MU-MIMO) systems with hundreds or even thousands of antennas at the enhanced-mobile broadband station (e-MBBS) has gained considerable attention in the research community and industry for emerging applications such as millimeter-wave (mm-wave) communications, 5G and Beyond, Beamforming and spatial division multiple access (SDMA) and IoT and Wearable Devices. In this paper, we propose a novel low-complexity detection algorithm, namely lattice reduction associated reactive Tabu search (LR-RTS), capable of providing near-optimal detection performances by mitigating both the inter-antenna interference (IAI) and multi-user interference (MUI). The lattice reduction (LR)-based precoding scheme is first incorporated by the mobile user to suppress the IAI. Then, the novel LR-associated RTS detection algorithm is used at the e-MBBS to mitigate the MUI. The initial signal vector of this algorithm is chosen from the solution of the LR pre-coded ZF detector. Simulation results and comparisons with state-of-the-art methods show that the proposed solution outperforms heuristic search-based algorithms, namely likelihood ascent search (LAS) and linear detection methods like zero-forcing (ZF). In addition, our method offers a better tradeoff between performance and computational complexity for systems with a massive number of antennas and higher-order QAM modulations, showing a performance gain between 2dB and 9dB versus the conventional techniques.