DeepHSIC：面向混合下行链路IM-NOMA的基于深度学习的信号检测器

中文摘要 AI 辅助

本文提出DeepHSIC作为混合下行链路IM-NOMA传输的神经接收机。所考虑的方案将功率域NOMA与复合OFDM/OFDM-IM波形相结合，使用户信息共同映射到星座符号、子载波索引模式和不同功率电平上。尽管最大似然检测可为该模型实现强可靠性，但它的搜索空间随用户和子载波数量的增加而快速增长。传统SIC可减轻部分负担，但其连续消除仍可能累积误差，且未充分利用IM-NOMA信号的结构。为解决此局限，所提出的检测器将专用深度神经网络模块嵌入接收机，并用学习到的推理块替换计算需求最高的SIC操作。该接收机针对瑞利衰落信道进行训练，使用预处理的信道输出特征恢复用户符号。仿真结果表明，在完美和不完美CSI两种情况下，DeepHSIC的误码率性能接近基于模型的检测器，同时检测时间显著更低。这些结果表明，学习到的SIC式检测是可扩展混合下行链路IM-NOMA接收机的实用候选方案。

英文摘要

DeepHSIC is introduced as a neural receiver for hybrid downlink IM-NOMA transmission. The considered scheme combines power-domain NOMA with a composite OFDM/OFDM-IM waveform, so that user information is mapped jointly onto constellation symbols, subcarrier-index patterns, and different power levels. Although maximum-likelihood detection can achieve strong reliability for this model, its search space grows rapidly with the number of users and subcarriers. Conventional SIC reduces part of this burden, but its sequential cancellation may still accumulate errors and does not fully exploit the structure of IM-NOMA signals. To address this limitation, the proposed detector embeds dedicated deep neural network modules into the receiver and replaces the most computationally demanding SIC operations with learned inference blocks. The receiver is trained for Rayleigh fading channels and uses preprocessed channel-output features to recover user symbols. Simulation results show that DeepHSIC reaches BER performance close to model-based detectors under both perfect and imperfect CSI while requiring substantially lower detection time. These results indicate that learned SIC-style detection is a practical candidate for scalable hybrid downlink IM-NOMA receivers.

发表机构

• National Economics University（越南国家经济大学）
• Hanoi University of Science and Technology（河内科技大学）
• Northeastern University（东北大学）
• Phenikaa University（菲尼亚卡大学）
• VNPT Group（VNPT集团）

机构由 AI 辅助整理，请以论文原文为准。