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For massive multiple-input multiple-output (MIMO) systems, linear minimummean-square error (MMSE) detection has been shown to achieve near-optimalperformance but suffers from excessively high complexity due to the large-scalematrix inversion. Being matrix inversion free, detection algorithms based onthe Gauss-Seidel (GS) method have been proved more efficient than conventionalNeumann series expansion (NSE) based ones. In this paper, an efficient GS-basedsoft-output data detector for massive MIMO and a corresponding VLSIarchitecture are proposed. To accelerate the convergence of the GS method, anew initial solution is proposed. Several optimizations on the VLSIarchitecture level are proposed to further reduce the processing latency andarea. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGAfor a 128 base-station antenna and 8 user massive MIMO system show that ourGS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSEerror-rate performance. Our implementation results demonstrate that theproposed solution has advantages over existing designs in terms of complexityand efficiency, especially under challenging propagation conditions.

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