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

TL;DR

This paper introduces a novel multi-target localization method using intelligent reflecting surfaces (IRSs) mounted on targets, optimizing their configuration to improve accuracy in cellular networks for autonomous driving and transportation.

Contribution

It proposes a new IRS-based localization framework that jointly optimizes target association, IRS phase shifts, and dwell time to enhance accuracy and reliability.

Findings

Achieves improved localization accuracy through joint optimization.

Provides a closed-form solution for single-target localization with flexible base station placement.

Establishes a lower performance bound for the localization system.

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 Cram\'er-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 ...