H-ResGAN在智能反射面辅助通信系统中的信道估计

Hybrid loss based residual generative adversarial network for channel estimation in intelligent reflecting surface assisted communication systems

智能反射面(intelligent reflecting surface,IRS)辅助通信系统的信道维度较高,现有的信道估计方法须使用大量导频才能得到准确的信道矩阵.针对这一问题,提出了一种基于混合损失的残差生成对抗网络(hybrid loss based residual generative adversarial network,H-ResGAN)模型.H-ResGAN使用多个残差块来加深网络,可以充分提取信道特征,减缓梯度消失问题.同时,采用条件最小二乘损失和L1损失相结合的混合损失作为目标函数来提高训练的稳定性.仿真实验证明:H-ResGAN对环境噪声更具鲁棒性,估计误差显著低于传统方法;与传统的估计算法相比,H-ResGAN仅须发送少量导频就能获得准确的估计结果.

Intelligent reflecting surface(IRS)aided communication systems have high channel dimensionality and the existing channel estimation methods require a lot of pilots to obtain an accurate channel matrix.To address this problem,a hybrid loss based residual generative adversarial network(H-ResGAN)model is proposed.H-ResGAN uses multiple residual blocks to deepen the network,which can fully extract channel features and mitigate the gradient disappearance problem.At the same time,a hybrid loss combining least squares loss and L1 loss is adopted as the objective function to improve the stability of the training.Simulation experiments demonstrate that H-ResGAN is more robust to environmental noise and has significantly lower estimation errors than traditional methods.In addition,HResGAN can obtain accurate estimation results by sending only few pilots compared to traditional estimation algorithms.