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.

Wireless communication

MIMO broadcast


machine learning

Benchmark testing

deep learning

end-to-end learning

wireless communications

interference channel

Transmitting antennas

digital signal processing



Interference channels

MIMO communication


Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

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

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. 21 9 7287-7298

Coding for terabit-per-second fiber-optical communications (TERA)

European Commission (EC) (EC/H2020/749798), 2017-01-01 -- 2019-12-31.

Unlocking the Full-dimensional Fiber Capacity

Knut and Alice Wallenberg Foundation (KAW 2018.0090), 2019-07-01 -- 2024-06-30.

Subject Categories


Communication Systems

Signal Processing



More information

Latest update