Wireless Fingerprinting Localization in Smart Environments using Reconfigurable Intelligent Surfaces

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.