Next Generation Multimodal Semantic Communication Networks

Dear Colleagues, 

In the era of Internet of Everything, communication plays an important role in the collaborative computing tasks of multiple sensors and computing resources. It is necessary to study multimodal semantic communication in the future IoT paradigm, which dynamically adjusts the multimodal information (image, text, video, etc.) transmission strategy adapted to the task objective and the underlying changes in the communication environments.

In recent years, with the rapid development of deep learning, mobile edge computing, deep reinforcement learning, semantic communication, and other fields, traditional communication techniques have not been able to meet the requirements of large bandwidth and low delay in the next generation networks. Meanwhile, the semantic communication framework has shown its potential advantages in many fields, such as video conferencing and remote sensing, etc. Therefore, new algorithms, including multimodal data semantic understanding and processing, joint source-channel coding, etc., should be explored.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging theories and applications with advanced multimodal semantic communication networks and address recent breakthroughs from a theoretical and practical perspective. The topics of interest include, but not limited to:

• Cross-modal semantic analysis;
• Context awareness algorithms based on multimodal data;
• Task-oriented multimodal data transmission;
• Multitask semantic communications;
• Multimodal data fusion;
• Joint source-channel coding;
• Multimodal remote sensing data processing;
• AI-driven semantic communication networks.

Dr. Jie Guo
Prof. Dr. Bin Song 
Dr. Ru Huo
Dr. Dapeng Lan
Guest Editors

Manuscript Submission Information

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• multimodal data fusion
• semantic communication
• semantic understanding
• joint optimization algorithms