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
A Mathematical Theory for Learning Semantic Languages by Abstract Learners
Kuo-Yu Liao, Cheng-Shang Chang, Y. -W. Peter Hong
Agent-driven Generative Semantic Communication with Cross-Modality and Prediction
Wanting Yang, Zehui Xiong, Yanli Yuan, Wenchao Jiang, Tony Q.S. Quek, Merouane Debbah
Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels
Yanhu Wang, Shuaishuai Guo, Anming Dong, Hui Zhao
Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
Peigen Ye, Yaping Sun, Shumin Yao, Hao Chen, Xiaodong Xu, Shuguang Cui
Towards a satisfactory conversion of messages among agent-based information systems
Idoia Berges, Jesús Bermúdez, Alfredo Goñi, Arantza Illarramendi