Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems  

作　　者：Zhen Zhang Yuxiang Zhang Jianhua Zhang Feifei Gao 

机构地区：[1]State Key Lab of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]Institute for Artificial Intelligence,Tsinghua University,Beijing 100084,China

出　　处：《China Communications》2023年第6期100-115,共16页中国通信（英文版）

基　　金：supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102;in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086);in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。

摘　　要：In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

关 键 词：channel prediction time-varying channel conditional generative adversarial network multipath channel deep learning 

分 类 号：TN929.5[电子电信—通信与信息系统]