Cooperative Cellular Localization with Intelligent Reflecting Surface: Design, Analysis and Optimization

TL;DR

This work tackles the challenge of high-precision, low-latency localization in cellular networks by leveraging intelligent reflecting surfaces (IRS) mounted on targets to actively shape echo signals. It formulates a non-convex optimization to minimize the maximum $CRLB$ across targets through joint design of target association, IRS phase shifts, and dwell-time allocation, and provides tractable results for both single- and multi-target scenarios. A closed-form lower bound on $CRLB$ under flexible BS deployment is derived and shown to be independent of the exact number of BSs, guiding the design of phase-shift and time-allocation strategies; for single targets, a monotonic optimization framework with a Polyblock-based solution is proposed, while for multiple targets a low-complexity two-BS-per-target heuristic with interference-aware scheduling is developed. Simulations demonstrate substantial localization performance gains over benchmarks, reduced requirements on BS density and time slots, and practical guidance for deploying IRS-aided cooperative localization in vehicular networks.

Abstract

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem ...

Figures (9)

• Figure 1: Scenarios and protocol for IRS-assisted localization.
• Figure 2: Illustration of beam flattening for robust localization.
• Figure 3: Echo signals power at different locations.
• Figure 4: MSE comparison versus CRLB under different transmit power.
• Figure 5: Localization performance comparison versus different numbers of IRS elements.