论文标题
基于模型的深度学习接收器设计用于速率分类多个访问
Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access
论文作者
论文摘要
下一代无线通信系统需要有效和适应性干扰管理。为了应对这一挑战，依靠发射机上的多端速率 - 分拆（RS）在发射机和接收器的连续干扰取消（SIC）依靠多数访问（RS）进行了深入的研究，近年来已经在接收方（CSIR）和理想能力的模型和编码的SCHEM和CODINGS ACRINCE SCHEM下进行了完美的渠道信息的假设。为了评估其实际的性能，益处和限制，这项工作为基于基于模型的深度学习（MBDL）方法的实用RSMA接收器提出了一种新颖的设计，该方法旨在团结常规SIC接收器的简单结构以及深度学习技术的鲁棒性和模型的不可能。根据未编码的符号错误率（SER），通过链接级模拟（LLS）和平均训练开销评估MBDL接收器。同样，给出了与SIC接收器的比较，具有完美且不完美的CSIR。结果表明，MBDL接收器的表现优于SIC接收器，而CSIR不完美，因为它能够以纯数据驱动的方式生成非线性符号检测边界。
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 receiver 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.
