Positioning via Probabilistic Graphical Models in RIS-Aided Systems with Channel Estimation Errors

TL;DR

This paper introduces a probabilistic graphical model-based 6D localization method for indoor RIS-assisted systems that accounts for channel estimation errors, demonstrating improved positioning accuracy with RIS.

Contribution

It presents a novel Bayesian framework using NUTS for joint position and rotation estimation in RIS systems considering channel errors.

Findings

RIS significantly improves positioning accuracy

The proposed method approaches the CRLB for error bounds

Channel estimation errors are effectively incorporated into the model

Abstract

We propose a 6D Bayesian-based localization framework to estimate the position and rotation angles of a mobile station (MS) within an indoor reconfigurable intelligent surface (RIS)-aided system. This framework relies on a probabilistic graphical model to represent the joint probability distribution of random variables through their conditional dependencies and employs the No-U-Turn Sampler (NUTS) to approximate the posterior distribution based on the estimated channel parameters. Our framework estimates both the position and rotation of the mobile station (MS), in the presence of channel parameter estimation errors. We derive the Cramer-Rao lower bound (CRLB) for the proposed scenario and use it to evaluate the system's position error bound (PEB) and rotation error bound (REB). We compare the system performances with and without RIS. The results demonstrate that the RIS can enhance positioning accuracy significantly.