ADVANCED ALGORITHMS FOR RIS-ASSISTED WIRELESS COMMUNICATION SYSTEMS

Abstract

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for 6G wireless networks, offering the ability to reshape propagation environments and improve communication performance. Despite its potential, deploying RIS faces challenges in channel estimation, signal processing, and system integration. This thesis develops novel algorithmic solutions for RIS-assisted wireless communication systems, focusing on improving estimation accuracy, reducing complexity, and enabling integrated sensing and communication.

First, we develop UAMP-SBL-PCI, exploiting structured sparsity in RIS-assisted channels. By combining partial common support identification with unitary approximate message passing, it reduces complexity while improving accuracy. Simulations demonstrate excellent performance across various environments.

Second, we propose a unified framework for ULA RIS systems jointly performing user positioning and channel estimation. A vision transformer (ViT) enables adaptive positioning, while a position-based dictionary design reduces dictionary size and resolves the off-grid problem. A modified sparse Bayesian learning algorithm with early stopping prevents overfitting for static deployment scenarios.

Third, we extend this to dynamic environments with a three-stage joint channel estimation and positioning framework for UPA RIS systems, employing a graph attention network (GAT) for robust positioning, location-aware dictionary refinement, and meta-learning for rapid adaptation. An iterative refinement mechanism further reduces pilot overhead.

These contributions span from basic channel estimation to advanced integrated sensing and communication, enabling practical RIS implementation with reduced costs and improved accuracy for 6G systems.