RIS-Enhanced Semantic Communications Adaptive to User Requirements

Semantic communication, through the interpretation of the semantic meaning of transmitted data, effectively reduces the required bandwidth. However, current deep learning-based methods face limitations due to their reliance on joint source-channel coding and end-to-end training, hindering adaptability to new channels and user demands. In this study, we introduce the Reconfigurable Intelligent Surface-Semantic Communication (RIS-SC) framework as a solution. This framework dynamically allocates semantic content, leveraging varying degrees of RIS assistance to cater to the evolving needs of users. It takes into account factors such as user mobility and obstacles in the line of sight, enabling the RIS resource to preserve essential semantics even in challenging channel conditions. While this ensures the preservation of core semantics in difficult channel conditions, it may also lead to the loss of some non-essential semantic details under extreme conditions. To counteract this, we have incorporated a reconstruction method that deduces the missing semantic elements, thereby enhancing visual understanding. The RIS-SC framework stands out for its adaptability, ensuring optimal resource distribution for users under favorable conditions and maintaining visual clarity in challenging scenarios. Simulations validate the effectiveness and adaptability of our approach in diverse channel conditions and user demands.