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
Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources
Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Carsten Bockelmann, Armin Dekorsy
IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation
Lingyi Wang, Wei Wu, Fuhui Zhou, Zhijin Qin, Qihui Wu
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang
Semantic Communication and Control Co-Design for Multi-Objective Correlated Dynamics
Abanoub M. Girgis, Hyowoon Seo, Mehdi Bennis
SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model
Kexin Zhang, Lixin Li, Wensheng Lin, Yuna Yan, Wenchi Cheng, Zhu Han