这份研究论文探讨了在近场通信中使用超大规模天线阵列 (ELAA) 时面临的干扰管理挑战，尤其是在发射机信道状态信息 (CSIT) 不完美的情况下。为了解决这些挑战，作者们提出了一种深度学习 (DL) 辅助的速率拆分多址 (RSMA) 方案，该方案旨在最大化用户遍历率的几何平均值，以优化系统吞吐量和公平性。文章详细阐述了这种RSMA-ELAA 系统的数学模型和架构，特别是名为 GruCN 的创新型 DL 模型，它结合了门控循环单元 (GRU) 和卷积神经网络 (CNN) 的优点。模拟结果表明，与传统方案相比，所提出的 RSMA 和 GruCN 方法在不完美 CSIT 条件下展现出卓越的性能、公平性和鲁棒性，并且显著提高了处理效率。

Rate-Splitting Multiple Access for Near-Field Communications with Imperfect CSIT and SIC

Abstract:

Extremely Large-scale Antenna Array (ELAA) is increasingly recognized as a promising solution for enhancing spectral efficiency and spatial resolution in the 6G mobile system. However, realizing these benefits necessitates the development of sophisticated interference management strategies, which typically rely on perfect Channel State Information at the Transmitter (CSIT) and involve computationally intensive operations. In real-world scenarios, perfect CSIT is typically infeasible due to inherent channel estimation errors and hardware impairments, which also lead to imperfect Successive Interference Cancellation (SIC). Additionally, the computational complexity associated with precoding schemes poses a formidable challenge. To address these issues, this study proposes a Deep Learning (DL)-assisted Rate-Splitting Multiple Access (RSMA) scheme for ELAA systems. The primary objective is to maximize the geometric mean of ergodic user-rates under imperfect CSIT and SIC, thereby optimizing both fairness and system throughput. Given the prohibitively high computational complexity of conventional optimization approaches to address this optimization problem, we introduce a DL model, named GruCN, to optimize precoder design. Simulation results demonstrate that the proposed RSMA-enabled ELAA system achieves better performance in terms of fairness and robustness under imperfect CSIT. Moreover, the GruCN model exhibits remarkable efficiency and effectiveness in precoder optimization.

Published in: IEEE Transactions on Communications ( Early Access )

Page(s): 1 - 1

Date of Publication: 03 July 2025

ISSN Information:

Print ISSN: 0090-6778

Electronic ISSN: 1558-0857