Abstract
Reconfigurable Intelligent Surfaces (RISs) promise improved, secure, and more efficient wireless communications. One less understood aspect relates to the benefits of RIS towards wireless localization and positioning of mobile users and devices. In this paper we propose and demonstrate two practical solutions that exploit the diversity offered by RIS-enhanced indoor environments and to select RIS state configurations that generate easily differentiable radio maps for use with wireless fingerprinting localization estimators. Specifically, we first investigate supervised learning feature selection methods to prune the large state space of the RIS, thus reducing complexity and enhancing localization accuracy and device position acquisition time. We then analytically derive noise correlated heuristics that can further reduce the computational complexity of our proposed solution. Finally, we validate and benchmark our proposed solutions through accurate end-to-end models and computer simulations while demonstrating an average localization accuracy improvement of about 33%. Our explorations thus demonstrate how and why accuracy improvements are achieved and also hint towards how these can be further enhanced in practical localization settings while utilizing more than one RIS.
Highlights
R ECONFIGURABLE intelligent surface (RIS) technology has given rise to the concept of “smart radio environments" [1] unlocking the engineering of the wireless propagation environment itself - a key stepping stone towards the sixth generation (6G) mobile network vision
In a 6G architecture, it is envisaged that the propagation environment itself will be available as a service to improve network performance [2] through RISs which essentially operate to controllably back-scatter electromagnetic (EM) signals originating from a network of traditional access points (APs)
This paper focuses on RIS-enhanced wireless fingerprinting localization (WFL) and proposes two practical solutions for improving localization accuracy while reducing computational complexity
Summary
R ECONFIGURABLE intelligent surface (RIS) technology has given rise to the concept of “smart radio environments" [1] unlocking the engineering of the wireless propagation environment itself - a key stepping stone towards the sixth generation (6G) mobile network vision. The paper is structured as follows: in Sec. II we summarize recent works on wireless localization with a focus on RISenhanced environments; in Sec. III we present our system model and problem statement while introducing the notation that we will use throughout the rest of the paper; in Sec. IV we provide an overview of the end-to-end method used to simulate and generate different radio maps as a result of different RIS states; in Sec. V we detail our first proposed solution for selecting a reduce RIS state set leveraging a supervised machine learning framework; in Sec. VI we detail two heuristic state selection solutions, a naive one and a more sophisticated one leveraging noise correlations; in Sec. VII present several computer simulation experiments that validate and benchmark the proposed solutions; and in Sec. VIII we summarize our results, discuss their implications and suggest future research questions
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