RIS-Enhanced Semantic Image Transmission Based on Reinforcement Learning
Peiwen Jiang, Chao-Kai Wen, Shi Jin, Xiao Li, Geoffrey Ye Li · 2023
Semantic communication can significantly reduce transmission payload by sending only semantic information related to the task. However, existing end-to-end trained semantic studies degrade under extreme channel environments, while reconfigurable intelligent surface (RIS) technology offers a potential solution for realizing channel customization. In this work, we propose a reconfigurable RIS-enhanced semantic communication framework called RIS-SC. This framework allows for customization of the channel environment based on the user's requirements for different semantic parts, rather than relying solely on the conventional bit error rate requirement. Using reinforcement learning, the RIS controller interacts with varying channels to meet the user's different requirements. The RIS controller adaptively protects important semantic parts by adjusting the channel conditions. Simulation results demonstrate that the proposed RIS-SC framework can adapt to different channel environments and improve task performance under varying requirements, such as vertical semantic or true image reconstruction.