Edge Large AI Model Empowered Cognitive Multimodal Semantic Communication System

Transmitting multimodal data through semantic communication offers a promising way to enhance the quality of experiences. However, existing single-modal semantic communication systems struggle to efficiently support multimodal data transmission. Additionally, users have different communication requirements for different modalities, while existing work lacks the capability to generate personalized communication schemes tailored to diverse requirements. In this paper, we propose a cognitive multimodal semantic communication system. At its core is a cognitive semantic communication agent (CSCA) powered by edge large AI model (LAM), enabling low-latency modality alignment and natural language intent understanding. The CSCA integrates a cognitive communication planning algorithm that leverages intent cognition and environment cognition to create personalized communication schemes for users. Experimental results demonstrate that our system outperforms baseline systems in terms of semantic accuracy, intent satisfaction rate and communication latency.