  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.
