Adaptive-Awareness for RIS-enhanced Semantic Communications (RISemCom) in Dynamic Random Environment

In this paper, we propose a multi-SNR adaptive Semantic Communication (SemCom) System based on Reconfigurable Intelligent Surface (RIS) to solve the problem of insufficient adaptability of traditional SemCom in dynamic channel environments. We firstly design a RIS-enhanced Semantic Communication (RISemCom) System that innovatively combines a programmable wireless environment with Deep Learning (DL) to achieve joint optimization of channel environment and semantic feature extraction. Next, two training algorithms are proposed: Dynamic Random Environment Adaptive Multi-SNR (DREAMS) algorithm and Two-Stage Training (TST) algorithm. The DREAMS dynamically adjusts SNR values during training, allowing a single model to adapt to a wide range of SNR conditions while significantly reducing deployment complexity. The TST serves as a comparison baseline, providing a dedicated optimized model for each specific SNR environment. Numerical results are demonstrated to confirm that the DREAMS algorithm maintains excellent performance across a wide range of SNRs with a single model, and significantly improves the PSNR and SSIM metrics compared to traditional methods under low SNR conditions. The performance gain is particularly notable in challenging low SNR environments, proving the system’s robustness in adverse channel conditions. This work not only expands the applicability of SemCom but also provides new insights for reliable communication in variable channel environments in future 6G networks.