VLSI design of large-scale soft-output MIMO detection using conjugate gradients

TLDR: This work proposes an FPGA design for soft-output data detection in orthogonal frequency-division multiplexing (OFDM)-based large-scale (multi-user) MIMO systems that uses a modified version of the conjugate gradient least square (CGLS) algorithm.

Abstract: We propose an FPGA design for soft-output data detection in orthogonal frequency-division multiplexing (OFDM)-based large-scale (multi-user) MIMO systems. To reduce the high computational complexity of data detection, our design uses a modified version of the conjugate gradient least square (CGLS) algorithm. In contrast to existing linear detection algorithms for massive MIMO systems, our method avoids two of the most complex tasks, namely Gram-matrix computation and matrix inversion, while still being able to compute soft-outputs. Our architecture uses an array of reconfigurable processing elements to compute the CGLS algorithm in a hardware-efficient manner. Implementation results on Xilinx Virtex-7 FPGA for a 128 antenna, 8 user large-scale MIMO system show that our design only uses 70% of the area-delay product of the competitive method, while exhibiting superior error-rate performance.