3D Gaussian Radiation Field Modeling for Integrated RIS-FAS Systems: Analysis and Optimization

2025-11-03

AI Summary

Joint optimization of fluid antenna systems (FAS) and reconfigurable intelligent surfaces (RIS) under fast-fading channels suffers from high computational complexity, slow convergence, and heavy pilot overhead. Method: This paper proposes a real-time co-optimization framework based on three-dimensional Gaussian radiation field modeling. By equivalently representing obstacles as virtual transmitters, the framework decouples learning of channel amplitude and phase responses and drives alternating optimization of fluid antenna position and RIS phase shifts using electromagnetic field information—eliminating iterative search and dense pilot requirements. Contribution/Results: The approach significantly improves channel prediction accuracy and optimization convergence speed. Experimental results demonstrate superior performance in terms of minimum achievable rate compared to state-of-the-art methods, validating its effectiveness and practicality in dynamic wireless environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The integration of reconfigurable intelligent surfaces (RIS) and fluid antenna systems (FAS) has attracted considerable attention due to its tremendous potential in enhancing wireless communication performance. However, under fast-fading channel conditions, rapidly and effectively performing joint optimization of the antenna positions in an FAS system and the RIS phase configuration remains a critical challenge. Traditional optimization methods typically rely on complex iterative computations, thus making it challenging to obtain optimal solutions in real time within dynamic channel environments. To address this issue, this paper introduces a field information-driven optimization method based on three-dimensional Gaussian radiation-field modeling for real-time optimization of integrated FAS-RIS systems. In the proposed approach, obstacles are treated as virtual transmitters and, by separately learning the amplitude and phase variations, the model can quickly generate high-precision channel information based on the transmitter's position. This design eliminates the need for extensive pilot overhead and cumbersome computations. On this framework, an alternating optimization scheme is presented to jointly optimize the FAS position and the RIS phase configuration. Simulation results demonstrate that the proposed method significantly outperforms existing approaches in terms of spectrum prediction accuracy, convergence speed, and minimum achievable rate, validating its effectiveness and practicality in fast-fading scenarios.

Problem

• Joint optimization of FAS positions and RIS phase configuration
• Real-time optimization in fast-fading wireless channel conditions
• Eliminating complex iterative computations and extensive pilot overhead

Innovation

• 3D Gaussian radiation field modeling for channel information
• Separate learning of amplitude and phase variations
• Alternating optimization of FAS positions and RIS phases