RIS-Enhanced Semantic Communications Adaptive to User Requirements

TL;DR

This paper introduces RIS-SC, a framework that uses reconfigurable intelligent surfaces to adapt semantic communication to changing user needs and channel conditions, enhancing efficiency and robustness.

Contribution

It proposes a novel RIS-assisted semantic communication framework that dynamically allocates resources based on user requirements and environmental conditions.

Findings

RIS-SC effectively adapts to diverse channel conditions.

The reconstruction method improves visual quality in challenging scenarios.

RIS resource allocation enhances multi-user efficiency.

Abstract

Semantic communication significantly reduces required bandwidth by understanding semantic meaning of the transmitted. However, current deep learning-based semantic communication methods rely on joint source-channel coding design and end-to-end training, which limits their adaptability to new physical channels and user requirements. Reconfigurable intelligent surfaces (RIS) offer a solution by customizing channels in different environments. In this study, we propose the RIS-SC framework, which allocates semantic contents with varying levels of RIS assistance to satisfy the changing user requirements. It takes into account user movement and line-of-sight obstructions, enabling the RIS resource to protect important semantics in challenging channel conditions. The simulation results indicate reasonable task performance, but some semantic parts that have no effect on task performances are abandoned under severe channel conditions. To address this issue, a reconstruction method is also introduced to improve visual acceptance by inferring those missing semantic parts. Furthermore, the framework can adjust RIS resources in friendly channel conditions to save and allocate them efficiently among multiple users. Simulation results demonstrate the adaptability and efficiency of the RIS-SC framework across diverse channel conditions and user requirements.