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
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener
The Internet of Senses: Building on Semantic Communications and Edge Intelligence
Roghayeh Joda, Medhat Elsayed, Hatem Abou-zeid, Ramy Atawia, Akram Bin Sediq, Gary Boudreau, Melike Erol-Kantarci, Lajos Hanzo
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Jihong Park, Jinho Choi, Seong-Lyun Kim, Mehdi Bennis
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Nguyen Cong Luong, Quoc-Viet Pham, Thien Huynh-The, Van-Dinh Nguyen, Derrick Wing Kwan Ng, Symeon Chatzinotas
WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery
Le Xia, Yao Sun, Chengsi Liang, Daquan Feng, Runze Cheng, Yang Yang, Muhammad Ali Imran
WITT: A Wireless Image Transmission Transformer for Semantic Communications
Ke Yang, Sixian Wang, Jincheng Dai, Kailin Tan, Kai Niu, Ping Zhang
Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective
Yulin Shao, Deniz Gunduz
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Yining Wang, Mingzhe Chen, Tao Luo, Walid Saad, Dusit Niyato, H. Vincent Poor, Shuguang Cui