In the realm of Semantic Communication (SC), an immersive experience can be enhanced by integrating multimodal signals such as text, audio, images, and video. However, challenges like data heterogeneity, semantic ambiguity, and signal fading pose significant issues. The research paper, “Large AI Model Empowered Multimodal Semantic Communications”, offers a novel solution using advancements in AI, specifically Multimodal Language Models (MLM) and Large Language Models (LLM).

Key elements of the proposed framework include:

• MLM-based Multimodal Alignment (MMA): Leverages MLMs to transform data between multimodal and unimodal formats while preserving semantic consistency.

• Personalized LLM-based Knowledge Base (LKB): Aids in personalized semantic extraction or recovery to address semantic ambiguity.

• Conditional Generative adversarial networks-based channel Estimation (CGE): Improves channel state information acquisition to combat fading channels in SC.

The significance lies in the improvement of user experience within SC through innovative AI model integration. This research can pave the way for advanced applications such as better virtual reality communication and personalized content delivery systems.