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

2026-02-24

AI Summary

This work addresses the limited accuracy of existing positioning methods for Passive Antenna Systems (PASS), which rely solely on received signal amplitude while discarding phase information. To overcome this limitation, we introduce phase-aware modeling into PASS for the first time, formulating a complex baseband signal model that incorporates path loss, waveguide attenuation, and distance-dependent phase rotation. We derive closed-form expressions for the Cramér–Rao Lower Bound (CRLB) and Position Error Bound (PEB) using the Fisher Information Matrix, and develop a maximum likelihood estimator that combines coarse grid search with Levenberg–Marquardt optimization to solve the resulting non-convex problem. Experimental results demonstrate that the proposed phase-aware approach significantly outperforms existing amplitude-only schemes in localization accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Pinching Antenna Systems
• Localization
• Phase-aware
• CRLB
• Positioning Accuracy

Innovation

• Phase-aware localization
• Pinching antenna systems
• Cramer-Rao lower bound
• Maximum likelihood estimation
• Fisher information matrix