In this project, I explored the efficacy of generative artificial intelligence (AI) technique, particularly the generative adversarial network (GAN) model, to estimate the cascaded channels of reconfigurable intelligent surface (RIS)-aided wireless communication systems. The findings prove that the proposed GAN-based optimized channel estimation algorithm 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 a convolutional neural network (CNN)-based method.

This work investigates the non-trivial problem of estimating complex-valued Gaussian signals in an industrial Internet of Things (IIoT) environment, where the channel fading is temporally correlated and modeled by a finite-state Markov process. To address the problem of estimating channel fading states and signals simultaneously, two deep learning (DL)-aided minimum mean square error (MMSE) estimation schemes are proposed and they exhibit a reasonable performance gap in normalized mean square error (NMSE) compared to the genie-aided scheme, which considers perfect knowledge of instantaneous channel fading states.

The primary aim of this work is to investigate the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems. Specifically, utilizing AI technique we propose a fine-tuned multi-step channel twin design process that can render CSI very close to the CSI of the actual environment.