无线电工程2025，Vol.55Issue(4)：739-748,10.DOI:10.3969/j.issn.1003-3106.2025.04.006
基于深度学习的仿射频分复用接收机设计
Deep Learning-based Receiver Design for Affine Frequency Division Multiplexing
摘要
Abstract
In this research,a deep learning-based receiver is designed for Affine Frequency Division Multiplexing(AFDM)over doubly selective fading channels.A Deep Neural Network(DNN)is constructed,trained offline using training data,and then deployed online at the receiver to output transmitted bits.Simulation results demonstrate that the proposed deep learning-based receiver achieves comparable Bit Error Rate(BER)performance with respect to the conventional channel estimation scheme when a guard interval exists between the pilot and data.Notably,as the guard interval between the pilot and data decreases,the BER performance of the deep learning-based receiver surpasses that of the traditional channel estimation scheme.Moreover,the proposed receiver exhibits greater robustness than existing schemes in the presence of pilot-data interference.关键词
仿射频分复用/深度神经网络/双选择性衰落信道/信道估计/符号检测Key words
AFDM/DNN/doubly selective fading channels/channel estimation/symbol detection分类
电子信息工程引用本文复制引用
黄鹏飞,李强..基于深度学习的仿射频分复用接收机设计[J].无线电工程,2025,55(4):739-748,10.基金项目
国家自然科学基金青年项目(62201228) （62201228）
广州市科技计划项目(2024A04J0191) （2024A04J0191）
中央高校基本科研业务费专项资金(21624405) National Natural Science Foundation of China(62201228) （21624405）
Guangzhou Municipal Science and Technology Project(2024A04J0191) （2024A04J0191）
Special Funds for Fundamental Research of Central Universities(21624405) （21624405）
