Beyond Covariance: Generative Spatial Correlation Modeling and Channel Interpolation for Fluid Antenna Systems

2026-04-17

AI Summary

This work addresses the significant overhead in channel state information estimation for fluid antenna systems, which arises from their dense port configuration. Existing covariance-based interpolation methods suffer from limited generalization and lack a theoretical performance bound. To overcome these limitations, the paper proposes a generative modeling framework that treats spatially sampled channels as a p-th order autoregressive Gaussian-Markov process. By leveraging the underlying state-space structure, the approach enables efficient interpolation that transcends conventional covariance-based paradigms. The authors establish, for the first time, a theoretical lower bound on channel interpolation error for fluid antenna systems and achieve optimal minimum mean square error (MMSE) estimation with linear computational complexity O(N) via Kalman filtering and smoothing. This framework offers a tunable trade-off between model complexity and accuracy, yielding a scalable, efficient, and theoretically grounded solution for channel reconstruction.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Matrix & Tensor Methods

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Fluid antenna systems (FAS) enable unprecedented spatial diversity within a compact form factor by flexibly switching among high-density antenna ports. To activate this capability, channel state information (CSI) over the ports is required, which implies high estimation overhead because the number of ports is usually very large. Conventional estimation schemes tend to first estimate the CSI for a small number of ports and then infer the CSI for the remaining antenna ports by interpolation exploiting correlation characteristics. However, existing correlation-based techniques lack generalization ability, and the fundamental limits of interpolating the CSI from sparse observations remain poorly understood. This paper adopts a generative modeling framework for characterizing the channel correlation among the FAS ports that departs fundamentally from covariance-descriptive models. Specifically, we represent the spatially sampled channel as a $p$th-order autoregressive (AR) Gauss-Markov process, which provides a principled and tunable tradeoff between model complexity and approximation accuracy via the AR order. In so doing, we can characterize the limits of channel interpolation by deriving the globally optimal minimum mean-square error (MMSE) estimator and establishing a tight lower bound on the minimum number of observations required to meet a prescribed reconstruction error. To reduce the complexity of MMSE estimation, we then exploit the state-space structure due to the ${\rm AR}(p)$ model and develop a Kalman filtering/smoothing-based interpolation algorithm. The resulting method attains the optimal MMSE performance with strictly linear complexity $\mathcal{O}(N)$ with $N$ denoting the number of ports, resulting in a scalable, efficient, and theoretically grounded framework for practical FAS channel reconstruction.

Problem

• Fluid Antenna Systems
• Channel State Information
• Spatial Correlation
• Channel Interpolation
• CSI Estimation

Innovation

• generative modeling
• autoregressive process
• channel interpolation
• Kalman filtering
• fluid antenna systems