Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

TL;DR

This paper benchmarks end-to-end machine learning approaches for MIMO communication systems, revealing their strengths, limitations, and specific scenarios where they outperform traditional methods, with open-source implementations provided.

Contribution

It provides a comprehensive comparison of ML-based MIMO methods against classical benchmarks across various scenarios, highlighting the true sources of gains and introducing a novel MU-MIMO approach.

Findings

ML gains often due to geometric shaping and resource allocation

ML-based MU-MIMO can outperform zero-forcing with centralized learning

Open-source implementations enable reproducibility and further research

Abstract

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.