Neural Networks-Enabled Channel Reconstruction for Fluid Antenna Systems: A Data-Driven Approach

2025-11-18

AI Summary

To address the challenges of low channel state information (CSI) estimation accuracy and high computational complexity in fluid antenna systems (FASs), this paper proposes a neural network-based, data-driven channel reconstruction method. The approach innovatively models electromagnetic port-to-port correlation features without requiring an explicit channel model, thereby accommodating both model-based and model-free scenarios. A lightweight network architecture is designed to ensure rapid convergence and strong robustness, achieving high reconstruction fidelity while significantly reducing computational overhead. Numerical experiments demonstrate that the proposed method outperforms state-of-the-art approaches in both mean squared error and inference latency—two critical performance metrics. This work establishes an efficient and practical paradigm for real-time CSI acquisition in FASs.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Fluid antenna systems (FASs) offer substantial spatial diversity by exploiting the electromagnetic port correlation within compact array spaces, thereby generating favorable small-scale fading conditions with beneficial channel gain envelope fluctuations. This unique capability opens new opportunities for a wide range of communication applications and emerging technologies. However, accurate channel state information (CSI) must be acquired before a fluid antenna can be effectively utilized. Although several efforts have been made toward channel reconstruction in FASs, a generally applicable solution to both model-based or model-free scenario with both high precision and efficient computational flow remains lacking. In this work, we propose a data-driven channel reconstruction approach enabled by neural networks. The proposed framework not only achieves significantly enhanced reconstruction accuracy but also requires substantially lower computational complexity compared with existing model-free methods. Numerical results further demonstrate the rapid convergence and robust reconstruction capability of the proposed scheme, outperforming current state-of-the-art techniques.

Problem

• Achieving accurate channel state information in fluid antenna systems
• Developing computationally efficient channel reconstruction methods
• Overcoming limitations of existing model-based and model-free approaches

Innovation

• Neural networks enable data-driven channel reconstruction
• Achieves high accuracy with low computational complexity
• Rapid convergence and robust reconstruction outperforms existing methods