Large AI Model Empowered Multimodal Semantic Communications

• URL: http://arxiv.org/abs/2309.01249v2
• Date: Sun, 4 Aug 2024 12:34:29 GMT
• Title: Large AI Model Empowered Multimodal Semantic Communications
• Authors: Feibo Jiang, Li Dong, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Xiaohu You,
• Abstract summary: We propose a Large AI Model-based Multimodal SC (LAMMSC) framework. We first present the Conditional-based Multimodal Alignment (MMA) that enables the transformation between multimodal and unimodal data. Then, a personalized LLM-based Knowledge Base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery. Finally, we apply the Generative adversarial network-based channel Estimation (CGE) for estimating the wireless channel state information.
• Score: 48.73159237649128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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