GAN-Based Channel Estimation for IRS-Aided Communication Systems

This paper proposes a generative adversarial network (GAN) based channel estimation scheme for intelligent reflecting surface (IRS)-aided single-input multiple-output (SIMO) communication systems. The proposed novel GAN-based deep learning technique is efficient to estimate channels in IRS-aided wireless communication systems with high accuracy. The generator of GAN can reproduce data whose distributions are similar to the actual underlying channel. Consequently, the proposed approach does not require the statistical distribution of the underlying channel to be known in advance. Simulation results prove that the proposed GAN-based channel estimation approach outperforms the conventional least square estimation (LSE) approach significantly in terms of estimation accuracy as well as provides better performance than a fully connected deep neural network (DNN) and convolutional neural network (CNN)-based methods.