Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication

TL;DR

This paper evaluates end-to-end autoencoder learning for MIMO and multi-user communication systems, comparing it with traditional methods, and reveals both its potential and interpretability challenges across various scenarios.

Contribution

It provides a comprehensive benchmark and analysis of autoencoder-based communication schemes, introducing a novel centralized learning approach for MIMO broadcast channels.

Findings

Autoencoders can outperform traditional schemes with proper baselines.

A new centralized learning autoencoder performs close to nonlinear vector-perturbation precoding.

Interpreting learned schemes reveals they often mimic conventional methods after transformations.

Abstract

End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without 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. Our particular focus is on memoryless multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case studies are considered: two point-to-point (closed-loop and open-loop MIMO) and two MU scenarios (MIMO broadcast and interference channels). For the point-to-point scenarios, we explain some of the performance gains observed in prior work through the selection of improved baseline schemes that include geometric shaping as well as bit and power allocation. For the MIMO broadcast channel, we demonstrate the feasibility of a novel AE method with centralized learning and decentralized execution. Interestingly, the learned scheme performs close to nonlinear vector-perturbation precoding and significantly outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls when interpreting learned communication schemes. In particular, we show that the AE for the considered interference channel learns to avoid interference, albeit in a rotated reference frame. After de-rotating the learned signal constellation of each user, the resulting scheme corresponds to conventional time sharing with geometric shaping.