Fundamental Analysis of Scalable Fluid Antenna Systems: Identifiability Limits, Information Theory, and Joint Processing

TL;DR

This paper develops an information-theoretic framework for scalable fluid antenna systems, analyzing their capacity limits, identifiability, and proposing a joint processing algorithm, validated through extensive simulations.

Contribution

It introduces a unified entropy-based analysis for S-FAS, deriving capacity hierarchies and identifying bottlenecks, advancing system design and understanding.

Findings

Derived a complete capacity hierarchy among configurations.

Identified an information bottleneck in sequential processing.

Proposed a joint MUSIC algorithm approaching capacity bounds.

Abstract

Unlike fixed-position arrays with static observation entropy, the scalable fluid antenna system (S-FAS) can dynamically adjust its aperture to form different observation spaces with configuration-dependent entropy budgets. This reconfigurability requires an information-theoretic framework beyond traditional algebraic identifiability analysis. This paper establishes an observation entropy framework for S-FAS, which unifies the derivation of identifiability limits, the diagnosis of processing bottlenecks, and system design optimization. For an S-FAS with mutual coupling suppression, we derive a complete capacity hierarchy among compressed, extended, and jointly stacked configurations. The entropy framework reveals that sequential two-stage processing suffers from an information bottleneck that restricts achievable capacity, while the noise entropy ratio can be used to distinguish fundamental performance limits from algorithmic deficiencies. A joint MUSIC algorithm is proposed to approach the theoretical joint capacity bound. Extensive Monte Carlo simulations, validated by both algebraic and information-theoretic criteria, verify the derived capacity hierarchy and identifiability boundaries.