Joint Channel Estimation and Positioning for RIS-Assisted Communications: An Integrated SBL and Deep Learning Framework

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for future 6G wireless communications. However, the passive nature of RIS and the high-dimensional cascaded channels pose significant challenges for channel estimation (CE), particularly in practical scenarios where decomposition dictionaries cannot be predefined. This paper proposes a novel three-stage joint CE and positioning (JCEP) framework for RIS-assisted communication systems. It first performs the initial CE based on a predefined row dictionary that exploits the structural properties of cascaded channels, and then conducts positioning based on the initial CE results. Finally, it refines the CE results by incorporating the positioning output to construct customized column dictionaries. The framework employs a unitary approximate message passing sparse Bayesian learning (UAMP-SBL) based channel estimator that adapts to both initial and CE refinement stages. For positioning, we design a graph attention network (GAT) to achieve robust positioning performance in dynamic environments. Furthermore, in the CE refinement, we introduce a location-aware dictionary design that leverages position priors to reduce computational overhead. Additionally, we employ meta-learning to enable rapid adaptation to new environments. Extensive simulations show that our framework achieves superior performance in CE and positioning accuracy with low complexity.

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