Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27173
Title: Large AI Model Empowered Multimodal Semantic Communications
Authors: Jiang, F
Peng, Y
Dong, L
Wang, K
Yang, K
Pan, C
You, X
Keywords: semantic communication;large AI models;LLM;MLM;knowledgebase;artificial intelligence (cs.AI);computation and language (cs.CL);machine learning (cs.LG)
Issue Date: 9-Sep-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Jiang, F. et al. (2024) 'Large AI Model Empowered Multimodal Semantic Communications', IEEE Communications Magazine, 63 (1), pp. 76 - 82. doi: 10.1109/MCOM.001.2300575.
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 networks-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.
URI: https://bura.brunel.ac.uk/handle/2438/27173
DOI: https://doi.org/10.1109/MCOM.001.2300575
ISSN: 0163-6804
Other Identifiers: ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
arXiv:2309.01249v2 [cs.AI]
Appears in Collections:Dept of Computer Science Research Papers

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