Large AI Model and Loss Variation-Empowered Dual-Importance Prioritized Semantic Transmission

In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.