High Performance Interference Suppression in Multi-User Massive MIMO Detector

In this paper, we propose a new nonlinear detector with improved interference suppression in Multi-User Multiple Input, Multiple Output (MU-MIMO) system. The proposed detector is a combination of the following parts: QR decomposition (QRD), low complexity users sorting before QRD, sorting-reduced (SR) K-best method and minimum mean square error (MMSE) pre-processing. Our method outperforms a linear interference rejection combining (IRC, i.e. MMSE naturally) method significantly in both strong interference and additive white noise scenarios with both ideal and real channel estimations. This result has wide application importance for scenarios with strong interference, i.e. when co-located users utilize the internet in stadium, highway, shopping center, etc. Simulation results are presented for the non-line of sight 3D-UMa model of 5G QuaDRiGa 2.0 channel for 16 highly correlated single-antenna users with QAM16 modulation in 64 antennas of Massive MIMO system. The performance was compared with MMSE and other detection approaches.Comment: Accepted for presentation at the VTC2020-Spring conferenc

LOW-COMPLEXITY AND HIGH-PERFORMANCE SOFT MIMO DETECTION BASED ON DISTRIBUTED M-ALGORITHM THROUGH TRELLIS-DIAGRAM

This paper presents a novel low-complexity multiple-input multipleoutput (MIMO) detection scheme using a distributed M-algorithm (DM) to achieve high performance soft MIMO detection. To reduce the searching complexity, we build a MIMO trellis graph and split the searching operations among different nodes, where each node will apply the M-algorithm. Instead of keeping a global candidate list as the traditional detector does, this algorithm keeps multiple small candidate lists to generate soft information. Since the DM algorithm can achieve good BER performance with a small M, the sorting cost of the DM algorithm is lower than that of the conventional K-best MIMO algorithm. The proposed algorithm is very suitable for high speed parallel processing.NokiaNokia Siemens Networks (NSN)XilinxNational Science Foundatio

Efficient VLSI Implementation of Soft-input Soft-output Fixed-complexity Sphere Decoder

Fixed-complexity sphere decoder (FSD) is one of the most promising techniques for the implementation of multipleinput multiple-output (MIMO) detection, with relevant advantages in terms of constant throughput and high flexibility of parallel architecture. The reported works on FSD are mainly based on software level simulations and a few details have been provided on hardware implementation. The authors present the study based on a four-nodes-per-cycle parallel FSD architecture with several examples of VLSI implementation in 4 × 4 systems with both 16-quadrature amplitude modulation (QAM) and 64-QAM modulation and both real and complex signal models. The implementation aspects and details of the architecture are analysed in order to provide a variety of performance-complexity trade-offs. The authors also provide a parallel implementation of loglikelihood- ratio (LLR) generator with optimised algorithm to enhance the proposed FSD architecture to be a soft-input softoutput (SISO) MIMO detector. To the authors best knowledge, this is the first complete VLSI implementation of an FSD based SISO MIMO detector. The implementation results show that the proposed SISO FSD architecture is highly efficient and flexible, making it very suitable for real application