# End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels

arXiv: https://arxiv.org/abs/2602.13446 · HTML: https://mlsanity.com/paper/2602.13446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication](https://mlsanity.com/paper/2203.07703/) (2022)
- [A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design](https://mlsanity.com/paper/2306.13423/) (2023)
- [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024)
- [Interference-Aware Super-Constellation Design for NOMA](https://mlsanity.com/paper/2503.07509/) (2025)

## Abstract (arXiv)

An end-to-end autoencoder (AE) framework is developed for downlink non-orthogonal multiple access (NOMA) over Rayleigh fading channels, which learns interference-aware and channel-adaptive super-constellations. While existing works either assume additive white Gaussian noise channels or treat fading channels without a fully end-to-end learning approach, our framework directly embeds the wireless channel into both training and inference. To account for practical channel state information (CSI), we further incorporate limited feedback via both uniform and Lloyd-Max quantization of channel gains and analyze their impact on AE training and bit error rate (BER) performance. Simulation results show that, with perfect CSI, the proposed AE outperforms the existing analytical NOMA schemes. In addition, Lloyd-Max quantization achieves superior BER performance compared to uniform quantization. These results demonstrate that end-to-end AEs trained directly over Rayleigh fading can effectively learn robust, interference-aware signaling strategies, paving the way for NOMA deployment in fading environments with realistic CSI constraints.