# Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication

arXiv: https://arxiv.org/abs/2203.07703 · HTML: https://mlsanity.com/paper/2203.07703/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [Bit Error and Block Error Rate Training for ML-Assisted Communication](https://mlsanity.com/paper/2210.14103/) (2022)
- [Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios](https://mlsanity.com/paper/2501.11229/) (2025)
- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)
- [DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems](https://mlsanity.com/paper/2212.07816/) (2022)
- [OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning](https://mlsanity.com/paper/2409.12964/) (2024)
- [Structured Superposition of Autoencoders for UEP Codes at Intermediate Blocklengths](https://mlsanity.com/paper/2508.07487/) (2025)
- [End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels](https://mlsanity.com/paper/2602.13446/) (2026)

## Abstract (arXiv)

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.