论文标题
TDD/FDD系统的对抗性培训辅助时变频道预测
Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems
论文作者
论文摘要
在本文中，提出了基于条件生成对抗网络（CPCGAN）的时变通道预测方法，以用于时间划分双工/频差双工（TDD/FDD）系统。 CPCGAN利用歧视器来计算预测的下行链路通道状态信息（CSI）与以前的上行链路CSI的条件约束下的实际样本分布之间的差异。 CPCGAN的发电机了解条件约束与预测的下行CSI之间的功能关系，并降低了预测的CSI和实际CSI之间的差异。 CPCGAN拟合数据分布的能力可以捕获通道的时间变化和多径特性。考虑到真实通道的传播特征，我们进一步开发了一个通道预测误差指标，以确定发电机是否达到最佳状态。模拟表明，与在同一用户速度下的现有方法相比，CPCGAN可以获得更高的预测准确性和更低的系统位错误率。
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.
