Efficient Pre-Conditioned Descent Search Detector for Massive MU-MIMO

Massive multi-user (MU) multiple-input multiple-output (MIMO) is an enabling technology for the next-generation wireless systems as it provides significant improvements in terms of spectral efficiency and per-user peak data rates compared to existing, small-scale MIMO. The presence of increasing antennas at the infrastructure base-station results, however, in high computational complexity for data detection, which requires low-complexity algorithms and efficient hardware designs. Descent search (DS) algorithms have been proposed to reduce complexity of data detection and enable efficient hardware implementations. However, their error-rate performance is not always satisfactory for channels that exhibit correlation or for systems with small base-station to user antenna ratios. In this paper, we propose a linear inequality constraint quadratic programming (LICQP) model, which enables the design of a constrained DS (CDS) detector that outperforms existing DS algorithms. To further improve the performance of CDS in the presence of challenging channel conditions, we propose a universal pre-conditioner. Theoretical analysis and numerical results indicate that CDS with pre-conditioning reduces the complexity over the state-of-the-art (SOA) by
$60\%$
. We develop a reference Xilinx Virtex-7 FPGA design, which demonstrates that our implementation achieves
$1.75\boldsymbol{\times }$
higher throughput (35 Mbps in a 128 antenna, 8 user system) at comparable hardware resource utilization.