Research Interest
- Machine Learning based communication
- AI-native/Semantic Communication
- Federated learning
- Integrated Sensing and Communication (ISAC)
- Upper mid-band channel measurement
[Journal]
[In preparation]
[J2] Yongjun Kim, H. Park, H. Kim, and J. Choi, "Hybrid Federated Learning for Noise-Robust Training," in preparation.
[Submitted / Under revision]
[J1] Yongjun Kim, J. Park, M. Bennis, and J. Choi, "Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation," submitted to IEEE Journal on Selected Areas in Communications (JSAC), May 2025.
[Accepted/Published]
[Conference]
[Submitted / Under revision]
[Accepted/Published]
[C2] Yongjun Kim, S. Seo, J. Park, M. Bennis, S. L. Kim, and J. Choi, "Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control", IEEE International Conference on Communication (ICC) 2024
[C1] Yongjun Kim, J. Lee, J. Kim, H. Joo, H W. Je, and J. Lee, "Channel State Feedback with Neural Networks: A Discrete Representation Learning Approach", IEEE Global Communications Conference (GLOBECOM) Workshop 2022.