GAN-Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems

Abstract

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately. In this paper, a generative adversarial network (GAN) variant based channel estimation method is proposed for XL-MIMO systems. Specifically, the GAN variant is developed to simultaneously estimate the LoS and NLoS path components of the XL-MIMO channel. The initially estimated channels instead of the received signals are input into the GAN variant as the conditional input to generate the XL-MIMO channels more efficiently. The GAN variant not only learns the mapping from the initially estimated channels to the XL-MIMO channels but also learns an adversarial loss. Moreover, we combine the adversarial loss with a conventional loss function to ensure the correct direction of training the generator. To further enhance the estimation performance, we investigate the impact of the hyper-parameter of the loss function on the performance of our method. Simulation results show that the proposed method outperforms the existing channel estimation approaches in the adopted channel model. In addition, the proposed method surpasses the Cram $\acute {\mathrm {e}}$ r-Rao lower bound (CRLB) under low pilot overhead.