发表机构
National Sun Yat-sen University; National Yang Ming Chiao Tung University（国立中山大学; 国立阳明交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于无监督深度神经网络的SCMA码本设计框架，利用莱斯因子相关准则统一AWGN与瑞利信道，所获近最优码本在广泛信道条件下显著优于基准方案，适用于超可靠通信。
AI 中文摘要
稀疏码多址接入（SCMA）是一种有前景的非正交复用技术，用于增强下一代通信网络中的连接性和频谱效率。本文基于一种新颖的深度神经网络（DNN）辅助框架，提出了近最优的上行SCMA码本设计。该DNN以无监督方式进行训练，其损失函数由生成码本的结构特征推导而来，从而无需标记训练数据。基于错误概率分析，针对上行瑞利衰落推导了依赖于莱斯因子的设计准则，并用于构建损失函数。所得准则自然地涵盖了加性高斯白噪声（AWGN）和瑞利衰落作为极限情况，为码本设计提供了统一框架。此外，推导了平均互信息（AMI）的上界和下界，为基于PEP的准则提供了信息论解释，并确立了其在高信噪比区域与基于AMI下界的准则的渐近一致性。分析进一步揭示了当信道从瑞利衰落过渡到AWGN时，主导设计因子如何演变。数值结果表明，所提出的近最优码本在广泛的莱斯因子范围内相对于代表性基准方案取得了显著的性能增益，并在错误平层区域表现出优异的错误率性能，使其特别适用于超可靠应用。
英文摘要
Sparse Code Multiple Access (SCMA) is a promising non-orthogonal multiplexing technique for enhancing connectivity and spectral efficiency in next-generation communication networks. This paper proposes near-optimal uplink SCMA codebook designs based on a novel deep neural network (DNN)-assisted framework. The DNN is trained in an unsupervised manner, with a loss function derived from the structural characteristics of the generated codebooks, thereby eliminating the need for labeled training data. Based on error-probability analysis, Rician-factor-dependent design criteria are derived for uplink Rician fading and used to formulate the loss function. The resulting criteria naturally encompass AWGN and Rayleigh fading as limiting cases, providing a unified framework for codebook design. Moreover, upper and lower bounds on the average mutual information (AMI) are derived, providing an information-theoretic interpretation of the PEP-based criterion and establishing its asymptotic consistency with the AMI-lower-bound-based criterion in the high-SNR regime. The analysis further reveals how the dominant design factors evolve as the channel transitions from Rayleigh fading to AWGN. Numerical results show that the proposed near-optimal codebooks achieve significant performance gains over representative benchmark schemes across a wide range of Rician factors and exhibit excellent error-rate performance in the error-floor region, making them particularly attractive for ultra-reliable applications.
Comments16 pages, 9 figures. Submitted to IEEE Transactions on Wireless Communications (first revision submitted)
