Feedforward Architectures for Decentralized Precoding in Massive MU-MIMO Systems

TL;DR

This paper introduces decentralized feedforward architectures for massive MU-MIMO precoding that reduce complexity and data rate requirements while maintaining high throughput and near-optimal error performance.

Contribution

It proposes novel decentralized WF precoders with feedforward architectures that parallelize processing across multiple computing units.

Findings

Achieves Gbit/s throughput on multi-GPU systems.

Maintains near-optimal error-rate performance.

Reduces interconnect and I/O data rates.

Abstract

Massive multi-user multiple-input multiple-output (MU-MIMO) enables significant gains in spectral efficiency and link reliability compared to conventional small-scale MIMO technology. Furthermore, linear precoders, e.g., using zero forcing or Wiener filter (WF) precoding, are sufficient to achieve excellent error-rate performance in the massive MU-MIMO downlink. However, these methods necessitate centralized processing at the base-station (BS), which causes (i) excessively high interconnect and chip input/output data rates, and (ii) high implementation complexity. We propose two feedforward architectures and corresponding decentralized WF precoders that parallelize precoding across multiple computing fabrics, effectively mitigating the issues of centralized approaches. To demonstrate the efficacy of our decentralized precoders, we provide implementation results on a multi-GPU system, which show that our solutions achieve throughputs in the Gbit/s regime while achieving (near-)optimal error-rate performance in the massive MU-MIMO downlink.