Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Paper i 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.

Författare

Jinxiang Song

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Christian Häger

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Jochen Schröder

Chalmers, Mikroteknologi och nanovetenskap (MC2), Fotonik

Tim O'Shea

Virginia Polytechnic Institute and State University

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

9322115

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

Ämneskategorier

Telekommunikation

Kommunikationssystem

Datorsystem

DOI

10.1109/GLOBECOM42002.2020.9322115

Mer information

Senast uppdaterat

2021-04-26