Geometry-Structured Channel Reconstruction for Conventional and Fluid Antenna Systems: Bayesian Inference and Fundamental Limits

2026-05-26

AI Summary

This work addresses the excessive overhead of channel state information acquisition in large-scale fluid antenna systems by leveraging the geometric structure of wireless channels. It parameterizes the port-domain channel using a small number of dominant propagation paths and develops a Bayesian inference framework for efficient channel reconstruction. The study establishes, for the first time, a theoretical error bound linking geometric modeling to unstructured channel recovery, and proposes the GS-EM-AMP algorithm, which adaptively learns statistical parameters by integrating geometric channel modeling, spatial correlation analysis, and expectation-maximization approximate message passing. In highly spatially correlated environments, GS-EM-AMP approaches the theoretical error bound, significantly reducing pilot overhead while enhancing reconstruction robustness.

Abstract

Accurate channel state information (CSI) acquisition is critical for exploiting the spatial flexibility of fluid antenna systems (FASs). However, port selection and transmission optimization require CSI over a large number of candidate port positions, making direct port-wise estimation prohibitively costly in terms of pilot overhead. This paper addresses this challenge through geometry-structured channel reconstruction, which exploits the fact that the port-domain CSI can be parameterized by a small number of dominant propagation paths. We first establish fundamental mean square error (MSE) and normalized MSE (NMSE) benchmarks for both geometry-structured and unstructured channel reconstruction, providing analytical references for evaluating the intrinsic benefit of geometric modeling in conventional antenna systems and FASs. Motivated by the strong spatial correlation induced by densely distributed fluid antenna ports, we further propose a Bayesian reconstruction framework, termed geometry-structured expectation-maximization approximate message passing (GS-EM-AMP). The proposed algorithm incorporates geometric channel structure into the EM-AMP procedure and adaptively learns unknown statistical parameters from noisy observations. Numerical results demonstrate that GS-EM-AMP achieves near-bound reconstruction accuracy while maintaining strong robustness against steering-domain correlation, thereby offering an efficient and reliable solution for large-scale CSI acquisition in FASs.

Problem

• channel state information
• fluid antenna systems
• pilot overhead
• port selection
• CSI acquisition

Innovation

• geometry-structured channel reconstruction
• fluid antenna systems
• Bayesian inference
• GS-EM-AMP
• channel state information