High-Throughput Data Detection for Massive MU-MIMO-OFDM using Coordinate Descent

TL;DR

This paper introduces a novel coordinate descent-based soft-output data detection algorithm for massive MU-MIMO-OFDM systems, achieving high throughput and low complexity in FPGA implementations.

Contribution

It proposes a new equalization-based detection algorithm with FPGA designs supporting large-scale MU-MIMO-OFDM systems, optimizing performance and implementation complexity.

Findings

Near-optimal error-rate performance at low complexity

FPGA design outperforms existing solutions by 2.6x in throughput

Supports hundreds of antennas and thousands of subcarriers

Abstract

Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables.