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

TL;DR

This paper introduces an end-to-end autoencoder framework for downlink NOMA over Rayleigh fading channels, effectively learning interference-aware signals and analyzing the impact of limited CSI feedback on performance.

Contribution

It presents a novel end-to-end learning approach for NOMA that incorporates fading channels and limited CSI feedback, outperforming traditional schemes.

Findings

AE with perfect CSI outperforms existing NOMA schemes

Lloyd-Max quantization yields better BER than uniform quantization

End-to-end learning effectively adapts to Rayleigh fading environments

Abstract

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.