Semantic Communication
Semantic communication aims to transmit only the essential meaning of data, rather than raw bits, improving efficiency and bandwidth utilization. Current research focuses on integrating generative AI models, such as diffusion models and transformers, with techniques like federated learning and joint source-channel coding to achieve robust and efficient semantic encoding and decoding across various modalities (images, speech, video). This approach holds significant promise for enhancing communication in resource-constrained environments and enabling new applications in areas like autonomous driving, IoT, and satellite networks by prioritizing meaningful information and reducing transmission overhead.
Papers
Demo: Real-Time Semantic Communications with a Vision Transformer
Hanju Yoo, Taehun Jung, Linglong Dai, Songkuk Kim, Chan-Byoung Chae
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen, Deniz Gündüz, H. Vincent Poor