  Multimodal signals, including text, audio, image, and video, can be
integrated into Semantic Communication (SC) systems to provide an immersive
experience with low latency and high quality at the semantic level. However,
the multimodal SC has several challenges, including data heterogeneity,
semantic ambiguity, and signal distortion during transmission. Recent
advancements in large AI models, particularly in the Multimodal Language Model
(MLM) and Large Language Model (LLM), offer potential solutions for addressing
these issues. To this end, we propose a Large AI Model-based Multimodal SC
(LAM-MSC) framework, where we first present the MLM-based Multimodal Alignment
(MMA) that utilizes the MLM to enable the transformation between multimodal and
unimodal data while preserving semantic consistency. Then, a personalized
LLM-based Knowledge Base (LKB) is proposed, which allows users to perform
personalized semantic extraction or recovery through the LLM. This effectively
addresses the semantic ambiguity. Finally, we apply the Conditional Generative
adversarial network-based channel Estimation (CGE) for estimating the wireless
channel state information. This approach effectively mitigates the impact of
fading channels in SC. Finally, we conduct simulations that demonstrate the
superior performance of the LAM-MSC framework.
