Efficient Soft-Output Gauss-Seidel Data Detector for Massive MIMO Systems

For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on the Gauss-Seidel (GS) method have been proved more efficient than conventional Neumann series expansion-based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and eight user massive MIMO system show that our GS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over the existing designs in terms of complexity and efficiency, especially under challenging propagation conditions.

Automated summary

This paper proposes an efficient GS-based soft-output data detector and corresponding VLSI architecture for massive MIMO systems, achieving throughput of 732 Mb/s with close-to-MMSE error-rate performance. Optimization methods are used to reduce processing latency and area on the VLSI architecture level. The proposed solution shows advantages in complexity and efficiency over existing designs, especially under challenging propagation conditions.