Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview
This overview establishes semantic communication as an emerging field for AI-driven systems, charting research pathways but is incremental as it synthesizes existing ideas.
The paper addresses the challenge of extracting and regenerating semantic information in communication systems using deep generative models, highlighting their potential to enhance robustness and handle complex data.
Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems. Its challenge of extracting semantic information from the original complex content and regenerating semantically consistent data at the receiver, possibly being robust to channel corruptions, can be addressed with deep generative models. This ICASSP special session overview paper discloses the semantic communication challenges from the machine learning perspective and unveils how deep generative models will significantly enhance semantic communication frameworks in dealing with real-world complex data, extracting and exploiting semantic information, and being robust to channel corruptions. Alongside establishing this emerging field, this paper charts novel research pathways for the next generative semantic communication frameworks.