Phase-Aware Localization in Pinching Antenna Systems: CRLB Analysis and ML Estimation

TL;DR

This paper introduces a phase-aware localization method for pinching antenna systems that leverages both amplitude and phase information, deriving theoretical bounds and proposing an ML estimator with improved accuracy over amplitude-only approaches.

Contribution

It develops a comprehensive signal model, derives CRLB expressions, and proposes a two-stage ML estimator that jointly exploits amplitude and phase for enhanced localization accuracy.

Findings

Proposed phase-aware estimator outperforms amplitude-only methods.

Derived closed-form CRLB expressions for localization accuracy.

Numerical results confirm improved positioning precision.

Abstract

Pinching antenna systems (PASS) have recently emerged as a promising architecture for high-frequency wireless communications. In this letter, we investigate localization in PASS by jointly exploiting the received signal amplitude and phase information, unlike recent works that consider only the amplitude information. A complex baseband signal model is formulated to capture free-space path loss, waveguide attenuation, and distance-dependent phase rotation between the user and each pinching antenna. Using this model, we derive the Fisher information matrix (FIM) with respect to the user location and obtain closed-form expressions for the Cramer-Rao lower bound (CRLB) and the position error bound (PEB). A maximum likelihood (ML) estimator that jointly considers the received signal amplitude and phase is developed to estimate the unknown user location. Given the non-convexity of the estimation problem, a two-stage solution combining coarse grid search and Levenberg-Marquardt refinement is proposed. Numerical results demonstrate that the proposed phase-aware estimator outperforms existing amplitude-only method in terms of positioning accuracy.