Jingkai Ying

ORCID: 0009-0005-7920-8035
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About
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Research Areas
  • Cognitive Computing and Networks
  • Video Analysis and Summarization
  • Advanced Data Compression Techniques
  • Advanced Image and Video Retrieval Techniques
  • Robotics and Automated Systems

Tsinghua University
2024-2025

Semantic communication has shown great potential in boosting the effectiveness and reliability of communications. However, its systems to date are mostly enabled by deep learning, which requires demanding computing resources. This article proposes a framework for networks semantic system, aiming offer sufficient resources processing transmission. Key techniques including sampling reconstruction, semantic-channel coding, semantic-aware resource allocation optimization introduced based on...

10.1109/mnet.2024.3352031 article EN IEEE Network 2024-01-10

10.1109/icc51166.2024.10622487 article EN ICC 2022 - IEEE International Conference on Communications 2024-06-09

Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, synchronization and packet-level forward error correction multimodal semantics have not been well studied. Due to independent encoders, synchronizing features both time domains is a challenging problem. In this paper, we take facial video speech transmission as an example propose Synchronous Multimodal...

10.48550/arxiv.2408.04535 preprint EN arXiv (Cornell University) 2024-08-08

Semantic communication has shown great potential in boosting the effectiveness and reliability of communications. However, its systems to date are mostly enabled by deep learning, which requires demanding computing resources. This article proposes a framework for networks semantic system, aiming offer sufficient resources processing transmission. Key techniques including sampling reconstruction, semantic-channel coding, semantic-aware resource allocation optimization introduced based on...

10.48550/arxiv.2312.00740 preprint EN other-oa arXiv (Cornell University) 2023-01-01
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