端到端自编码器让诺玛在起伏信道中更抗干扰，适合实际通信场景。
End-to-End NOMA with Perfect and Quantized CSI Over Rayleigh Fading Channels
- 直接在瑞利衰落信道上训练自编码器，学习适应信道的信号星座图。
- 完美信道信息下性能超越传统诺玛方案，利德-马克斯量化优于均匀量化。
- 适用于有信道反馈限制的真实无线系统，为诺玛部署提供新路径。
针对瑞利衰落信道下的下行链路非正交多址接入（NOMA），本文提出一种端到端自编码器（AE）框架，可学习具备干扰感知与信道自适应特性的超星座图。现有研究或仅假设高斯白噪声信道，或未采用全端到端学习方式处理衰落信道。本框架将无线信道直接嵌入训练与推理过程。为考虑实际信道状态信息（CSI），引入了基于均匀量化和利德-马克斯（Lloyd-Max）量化的有限反馈机制，并分析其对AE训练与误码率（BER）性能的影响。仿真结果表明，在完美CSI条件下，所提AE性能优于现有解析式诺玛方案；且利德-马克斯量化相比均匀量化具有更优的BER表现。结果表明，直接在瑞利衰落信道上训练的端到端自编码器能有效学习鲁棒、干扰感知的信号策略，为在存在真实CSI约束的衰落环境中部署诺玛提供了可能。
原文摘要 · Abstract (English)
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.
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