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

TL;DR

This paper proposes a real-time optimization method for integrated RIS-FAS systems using 3D Gaussian radiation modeling, significantly improving accuracy and speed in fast-fading wireless environments.

Contribution

It introduces a novel 3D Gaussian radiation-field model for rapid channel estimation and joint optimization of FAS and RIS configurations in dynamic conditions.

Findings

Outperforms existing methods in spectrum prediction accuracy

Achieves faster convergence in optimization

Ensures higher minimum achievable rate

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.