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
Semantic Communication based on Large Language Model for Underwater Image Transmission
Weilong Chen, Wenxuan Xu, Haoran Chen, Xinran Zhang, Zhijin Qin, Yanru Zhang, Zhu Han
Synchronous Multi-modal Semantic Communication System with Packet-level Coding
Yun Tian, Jingkai Ying, Zhijin Qin, Ye Jin, Xiaoming Tao
Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
Haowen Wan, Qianqian Yang, Jiancheng Tang, Zhiguo shi
Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channels
Zian Meng, Qiang Li, Ashish Pandharipande, Xiaohu Ge
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication
Yuna Yan, Xin Zhang, Lixin Li, Wensheng Lin, Rui Li, Wenchi Cheng, Zhu Han
Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework
Kexin Zhang, Lixin Li, Wensheng Lin, Yuna Yan, Rui Li, Wenchi Cheng, Zhu Han