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

TL;DR

This paper introduces a novel adversarial training-based method, CPcGAN, for predicting time-varying channels in TDD/FDD systems, improving accuracy and reducing errors.

Contribution

It proposes a conditional GAN framework that effectively models the dynamic channel characteristics and includes a prediction error indicator for optimal generator performance.

Findings

Higher prediction accuracy than existing methods

Lower system bit error rate achieved

Effective modeling of time-varying and multipath channels

Abstract

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. CPcGAN 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.