用自编码器设计抗干扰的星座图，让非正交多址更稳定
Interference-Aware Super-Constellation Design for NOMA
- 用自编码器训练出接收端可区分的叠加星座图
- 在不同信道下比特误码率显著降低，无需逐次干扰消除
- 适合追求高可靠通信的5G/6G系统设计
非正交多址（NOMA）作为下一代多址技术备受关注，但采用有限星座输入时面临挑战。由于用户间干扰，叠加星座可能出现符号重叠，导致串行干扰消除（SIC）后误码率升高。本文提出基于自编码器的NOMA（AE-NOMA），通过训练生成接收端可区分的星座图，不受信道增益影响。该方法无需SIC，可直接使用最大似然检测。论文给出了架构、损失函数与训练策略，并通过多组测试验证了干扰感知星座在降低比特误码率方面的有效性，表明AE-NOMA能适应不同信道场景，具备实现高性能NOMA系统的潜力。
原文摘要 · Abstract (English)
Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet inputs faces challenges. Particularly, due to inter-user interference, superimposed constellations may have overlapping symbols leading to high bit error rates when successive interference cancellation (SIC) is applied. To tackle the issue, this paper employs autoencoders to design interference-aware super-constellations. Unlike conventional methods where superimposed constellation may have overlapping symbols, the proposed autoencoder-based NOMA (AE-NOMA) is trained to design super-constellations with distinguishable symbols at receivers, regardless of channel gains. The proposed architecture removes the need for SIC, allowing maximum likelihood-based approaches to be used instead. The paper presents the conceptual architecture, loss functions, and training strategies for AE-NOMA. Various test results are provided to demonstrate the effectiveness of interference-aware constellations in improving the bit error rate, indicating the adaptability of AE-NOMA to different channel scenarios and its promising potential for implementing NOMA systems
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