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

本研究解决了非正交多址接入（NOMA）中由于用户间干扰导致的符号重叠问题，从而导致较高的比特错误率。通过使用自编码器设计干扰感知超星座，本文提出了一种新方法，使得接收端能够识别出可区分的符号，从而有效改善比特错误率，展示了其在不同信道场景下的适应性和在实现NOMA系统中的潜在影响。

干扰感知超星座设计用于非正交多址接入