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

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, H-ResGAN can obtain accurate estimation results by sending only few pilots compared to traditional estimation algorithms.