COLoRIS: Localization-agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks

TL;DR

This paper introduces COLoRIS, a novel RIS-based localization method in 6G networks that accurately positions users without extra infrastructure, using learned models and opportunistic configurations, achieving low error rates.

Contribution

It presents a new RIS-based localization approach that is independent of specific configurations and leverages machine learning for accurate user positioning in 6G networks.

Findings

Achieves 5% positioning error in large-scale scenarios.

Low-complexity implementation attains 11% error with minimal energy increase.

Demonstrates effectiveness of RIS-based localization without additional infrastructure.

Abstract

The integration of Smart Surfaces in 6G communication networks, also dubbed as Reconfigurable Intelligent Surfaces (RISs), is a promising paradigm change gaining significant attention given its disruptive features. RISs are a key enabler in the realm of 6G Integrated Sensing and Communication (ISAC) systems where novel services can be offered together with the future mobile networks communication capabilities. This paper addresses the critical challenge of precisely localizing users within a communication network by leveraging the controlled-reflective properties of RIS elements without relying on more power-hungry traditional methods, e.g., GPS, adverting the need of deploying additional infrastructure and even avoiding interfering with communication efforts. Moreover, we go one step beyond: we build COLoRIS, an Opportunistic ISAC approach that leverages localization-agnostic RIS configurations to accurately position mobile users via trained learning models. Extensive experimental validation and simulations in large-scale synthetic scenarios show 5% positioning errors (with respect to field size) under different conditions. Further, we show that a low-complexity version running in a limited off-the-shelf (embedded, low-power) system achieves positioning errors in the 11% range at a negligible +2.7% energy expense with respect to the classical RIS.