Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
Journal article, 2022

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.

Benchmark testing

Transmitting antennas

digital signal processing

machine learning

interference channel

Autoencoders

Wireless communication

Interference channels

deep learning

MIMO broadcast

MIMO communication

Training

wireless communications

end-to-end learning

Receivers

Author

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Jochen Schröder

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Tim O'Shea

Virginia Polytechnic Institute and State University

Erik Agrell

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

IEEE Transactions on Wireless Communications

1536-1276 (ISSN)

Vol. In Press

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/TWC.2022.3157467

More information

Latest update

3/31/2022