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Semantic communication significantly reduces required bandwidth byunderstanding semantic meaning of the transmitted. However, current deeplearning-based semantic communication methods rely on joint source-channelcoding design and end-to-end training, which limits their adaptability to newphysical channels and user requirements. Reconfigurable intelligent surfaces(RIS) offer a solution by customizing channels in different environments. Inthis study, we propose the RIS-SC framework, which allocates semantic contentswith varying levels of RIS assistance to satisfy the changing userrequirements. It takes into account user movement and line-of-sightobstructions, enabling the RIS resource to protect important semantics inchallenging channel conditions. The simulation results indicate reasonable taskperformance, but some semantic parts that have no effect on task performancesare abandoned under severe channel conditions. To address this issue, areconstruction method is also introduced to improve visual acceptance byinferring those missing semantic parts. Furthermore, the framework can adjustRIS resources in friendly channel conditions to save and allocate themefficiently among multiple users. Simulation results demonstrate theadaptability and efficiency of the RIS-SC framework across diverse channelconditions and user requirements.

RIS-Enhanced Semantic Communications Adaptive to User Requirements