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

2024-06-11

IEEE Transactions on Mobile Computing

AI Summary

Conventional high-power localization methods for 6G integrated sensing and communication (ISAC) rely on external signal sources (e.g., GPS) and dedicated hardware, limiting deployment flexibility and energy efficiency. Method: This work proposes a GPS-free, infrastructure-free RIS-aided user localization paradigm, elevating reconfigurable intelligent surfaces (RIS) from passive reflectors to active sensing-communication co-design enablers. We introduce a localization-agnostic RIS configuration mechanism and develop an end-to-end lightweight learning framework integrating controllable reflection modeling, multi-dimensional channel fingerprint extraction, and embedded-edge inference optimization. Contribution/Results: Large-scale simulations demonstrate a positioning error as low as 5% of the operational area size. The lightweight model incurs only a 2.7% additional power overhead on embedded devices while maintaining a 11% positioning error—achieving a compelling trade-off among accuracy, energy efficiency, and practical deployability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• Localizing users in 6G networks without GPS or extra infrastructure
• Enabling accurate positioning via RIS and trained learning models
• Achieving low-energy, low-complexity ISAC with minimal positioning errors

Innovation

• Localization-agnostic RIS configurations for positioning
• Trained learning models for accurate user positioning
• Low-power embedded system with minimal energy expense