Rethinking Channel Estimation for Fluid Antenna Systems: A Physics-Informed Operator Learning
Abstract
Fluid antenna systems (FAS) leverage continuous reconfigurable apertures to transcend the spatial limits of fixed-position arrays, yet acquiring full-aperture channel state information (CSI) from sparse observations remains a formidable bottleneck. In this paper, we establish a continuous-field formulation for FAS by recasting channel estimation as a stochastic inverse field-reconstruction problem constrained by the projected Helmholtz model. Through electromagnetic information theory (EIT)-based spectral leakage analysis, we demonstrate that finite-aperture reconstruction induces an intrinsic convolutional relationship in the spectral domain, rendering the estimation task a fundamental frequency-domain inverse deconvolution problem. To solve this, we propose a U-shaped Fourier neural operator (UFNO) specifically tailored for noisy, underdetermined wireless environments. Furthermore, we leverage the Landau--Pollak theorem to derive an aperture-dependent EDoF bound that guides the spatial mode budget, and employ a physics-aware Sobolev loss to promote consistency in both channel values and spatial variations. Unlike conventional learning-based approaches, a single frozen UFNO can be directly evaluated at unseen spatial--frequency resolutions without resolution-specific retraining or explicit output interpolation. Extensive simulations show that our framework significantly outperforms state-of-the-art benchmarks in accuracy while achieving orders-of-magnitude lower inference latency than iterative solvers at high spatial--frequency resolutions.
Related
- Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification
- Single-step initialization of a 16-channel silicon nitride DWDM demultiplexer using a physics-informed digital twin
- PINNIES: An efficient physics-informed neural network framework for integral operator problems
- Exact spectral integrability of a finite-channel random Schrödinger operator: from fermionization of the Dyson--Riccati gas to Kontsevich integral
- Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction
BibTeX
@misc{wang2026rethinking,
title = {{Rethinking Channel Estimation for Fluid Antenna Systems: A Physics-Informed Operator Learning}},
author = {Zixin Wang and Lizhong Zheng and Yuanming Shi and Kaibin Huang and Khaled B. Letaief},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36787},
eprint = {2610.36787},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36787},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Rethinking Channel Estimation for Fluid Antenna Systems: A Physics-Informed Operator Learning
AU - Zixin Wang
AU - Lizhong Zheng
AU - Yuanming Shi
AU - Kaibin Huang
AU - Khaled B. Letaief
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36787
UR - https://arxiv.org/abs/2610.36787
AB - Fluid antenna systems (FAS) leverage continuous reconfigurable apertures to transcend the spatial limits of fixed-position arrays, yet acquiring full-aperture channel state information (CSI) from sparse observations remains a formidable bottleneck. In this paper, we establish a continuous-field formulation for FAS by recasting channel estimation as a stochastic inverse field-reconstruction problem constrained by the projected Helmholtz model. Through electromagnetic information theory (EIT)-based spectral leakage analysis, we demonstrate that finite-aperture reconstruction induces an intrinsic convolutional relationship in the spectral domain, rendering the estimation task a fundamental frequency-domain inverse deconvolution problem. To solve this, we propose a U-shaped Fourier neural operator (UFNO) specifically tailored for noisy, underdetermined wireless environments. Furthermore, we leverage the Landau--Pollak theorem to derive an aperture-dependent EDoF bound that guides the spatial mode budget, and employ a physics-aware Sobolev loss to promote consistency in both channel values and spatial variations. Unlike conventional learning-based approaches, a single frozen UFNO can be directly evaluated at unseen spatial--frequency resolutions without resolution-specific retraining or explicit output interpolation. Extensive simulations show that our framework significantly outperforms state-of-the-art benchmarks in accuracy while achieving orders-of-magnitude lower inference latency than iterative solvers at high spatial--frequency resolutions.
ER -