# Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

arXiv: https://arxiv.org/abs/2005.09718 · HTML: https://mlsanity.com/paper/2005.09718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 4 of 4)

- [SVD-Embedded Deep Autoencoder for MIMO Communications](https://mlsanity.com/paper/2111.02359/) (2021)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [End-to-End Autoencoder Communications with Optimized Interference Suppression](https://mlsanity.com/paper/2201.01388/) (2021)
- [Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI](https://mlsanity.com/paper/2310.15027/) (2023)

## Abstract (arXiv)

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 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.