Joint Channel Estimation and Localization in Pinching-Antenna OFDM Systems: The Blessing of Multipath

TL;DR

This paper presents a novel joint channel estimation and localization framework for pinching-antenna OFDM systems that effectively handles multipath effects, achieving near-optimal performance with reduced hardware complexity.

Contribution

It introduces a hybrid inference approach combining expectation propagation, OMP, and belief propagation-variational inference for accurate joint estimation in PASS systems.

Findings

Framework closely approaches the CRLB.

Performance comparable to multi-base station localization.

Reduces hardware complexity with fewer RF chains.

Abstract

Pinching-antenna systems (PASS) have recently attracted considerable attention owing to their capability of flexibly reconfiguring large-scale wireless channels. Motivated by this potential, we investigate the issue of joint localization and channel estimation for the uplink PASS in the presence of multipath dispersion. To this end, a comprehensive multi-user orthogonal frequency division multiplexing (OFDM) uplink PASS model is first established, where the use of a cyclic prefix (CP) enables the multipath-induced time-domain dispersion to be transformed into a set of superimposed sinusoids in the frequency domain. Building upon this model, we propose a hybrid inference framework capable of accurately estimating both channel parameters and user locations. Specifically, expectation propagation is first employed to mitigate multi-user interference, while the path delays are then extracted from noisy channel state information using an orthogonal matching pursuit (OMP) based approach, or a hybrid belief propagation-variational inference (BP-VI) algorithm. Then the estimated delays are subsequently refined through the embedded geometric information via an iterative localization procedure, wherein the estimated channel matrices are recursively fed back to EP. Furthermore, the Cramer-Rao lower bound (CRLB) is derived to characterize the fundamental estimation limits. Finally, simulation results validate that our proposed framework closely approaches the CRLB, with performance comparable to cooperative multi-base station localization, with significantly fewer RF chains and reduced hardware complexity.