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

2026-02-13

AI Summary

This work addresses the challenge of interference mitigation in non-orthogonal multiple access (NOMA) under Rayleigh fading channels with limited channel state information (CSI). To overcome the limitations of conventional NOMA schemes, the authors propose the first fully end-to-end trainable NOMA autoencoder framework that jointly optimizes interference-resilient, channel-adaptive super-constellations. The framework incorporates both uniform and Lloyd–Max quantization to emulate practical finite-rate CSI feedback. Experimental results demonstrate that, under perfect CSI, the proposed method outperforms existing NOMA approaches. Moreover, under quantized CSI, Lloyd–Max quantization yields significantly better performance than uniform quantization, thereby validating the feasibility and superiority of deep learning-driven NOMA for real-world deployment scenarios.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• NOMA
• Rayleigh fading
• CSI quantization
• end-to-end learning
• limited feedback

Innovation

• end-to-end autoencoder
• non-orthogonal multiple access (NOMA)
• Rayleigh fading channels
• quantized CSI
• super-constellations