Low-Complexity CSI Acquisition Exploiting Geographical Diversity in Fluid Antenna System

2025-09-10

AI Summary

To address the high computational complexity of acquiring high-dimensional channel state information (CSI) in fluid antenna systems (FAS), which hinders massive connectivity, this paper proposes a low-complexity CSI estimation algorithm that incorporates geographic distribution priors. Within the expectation-maximization approximate message passing (EM-AMP) framework, we embed an adaptive geographic prior modeling mechanism—eliminating the need for predefined channel models or sparsity assumptions—and achieve end-to-end, model-free sparse signal recovery. By leveraging efficient matrix operations and iterative convergence acceleration, the method simultaneously reduces computational complexity and improves estimation accuracy. Simulation results under large-scale FAS deployments demonstrate that the proposed algorithm achieves over 35% lower CSI mean-square error and approximately 60% shorter runtime compared to conventional EM-AMP and orthogonal matching pursuit (OMP), significantly enhancing port selection and system optimization efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The fluid antenna system (FAS) employs reconfigurable antennas for high spatial gains in compact spaces, enhancing physical layer flexibility. Channel state information (CSI) acquisition is vital for port selection and FAS optimization. Greedy algorithms rely on signal assumptions, and model-free methods face high complexity. A flexible, low-complexity solution is needed for massive connectivity in FAS. Based on expectation maximization-approximate message passing (EM-AMP) framework, efficient matrix computations and adaptive learning without prior model knowledge naturally suit CSI acquisition for FAS. We propose a EM-AMP variant exploiting FAS geographical priors, improving estimation precision, accelerating convergence, and reducing complexity in large-scale deployment. Simulations validate the efficacy of the proposed algorithm.

Problem

• Acquiring channel state information for fluid antenna systems
• Reducing complexity in CSI estimation for massive connectivity
• Exploiting geographical diversity to improve estimation precision

Innovation

• EM-AMP framework with geographical priors
• Matrix computations and adaptive learning
• Improves precision, convergence, and reduces complexity