Generative AI Enabled Semantic Communication
Speaker: Zhu Han – Houston, TX, United StatesTopic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Abstract
Semantic communication (SemCom), a prominent feature of 6G, aims to address communication problems at the semantic level by transferring semantic information accurately and efficiently. Advances in generative artificial intelligence (GAI), such as the development of large language models and improved generative capabilities, have significantly facilitated the implementation of SemCom. This talk presents three cases of GAI empowering SemCom: The first case is a Swin-Transformer-based dynamic SemCom system that optimizes semantic communication efficiency by dynamically adjusting the compression rate based on network conditions for multi-user scenarios with varying computing capacities. The second case is a federated learning framework designed to enhance global model performance in decentralized environments by leveraging Federated Local Loss (FedLol) for efficient aggregation, reduced communication overhead, and effective image reconstruction. The third case is an AI-generated content framework (AIGC-SCM) for remote monitoring, utilizing GAI to achieve high-fidelity reconstruction of compressed content while maintaining semantic consistency and optimizing energy efficiency. Experimental results and demo confirm the effectiveness of these methods and provide practical insights for integrating SemCom with GAI.About this Lecture
Number of Slides: 50Duration: 60 minutes
Languages Available: Chinese (Simplified), English
Last Updated:
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