Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Paper in proceeding, 2020

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 (MU-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.

Author

Jinxiang Song

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Christian Häger

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Jochen Schröder

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Tim O'Shea

Virginia Polytechnic Institute and State University

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

9322115

2020 IEEE Global Communications Conference, GLOBECOM 2020
Virtual, Taipei, Taiwan,

Subject Categories

Telecommunications

Communication Systems

Computer Systems

DOI

10.1109/GLOBECOM42002.2020.9322115

More information

Latest update

4/26/2021