Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.
翻译:为了应对这一挑战,近年来,对依赖发射机多通电分率和接收器连续取消干扰(SIC)的速率拉平多重存取(RSMA)系统进行了深入研究,尽管主要假设接收器的频道国家信息完美,以及理想的能力实现调制和编码计划;为了评估其实际性能、效益和在更现实条件下的限度,这项工作提出了基于基于基于模型的深层次学习(MBDDL)方法的实用的RSMA接收器的新设计,其目的是将传统的SIC接收器的简单结构与深层次学习技术的稳健性和模型的认知性统一起来。MBDL接收器以未编码的符号错误率(SER)和通过链接级别模拟(LLS)和平均培训管理费的吞吐率来评价。此外,对SIC接收器的精确和不完善性能进行了比较。结果显示,MBDL在以显著的测算速度超过CSIS的标志性能,在CSIS的测算中以不完善的纯度生成了不完善的CIS标准。