- About Us
- Welcome Message
- Vision
- Brief History
- Staff
- Contact Us
- Faculty / Research
- Faculty
- Research Labs
- Academics
- Curriculum
- Course Guide
- Graduate School
- Graduation Project
- Admissions
- Admissions
- Academic Affairs
- Academic Calendar
- Scholarships
- Graduation Requirements
- Student Clubs
- Academic Information
- Facility Reservations
- Community / News
- NEWS
- Notices
- Event
- Job Openings
- Forms
- KU Q&A
Community / News
NEWS
Professor Joongheon Kim Publishes Paper on Reinforcement Learning-Based Non-Terrestrial Networks in IEEE`s Highest-Impact-Factor Journal
관리자 2026.10.26 Views 85
Professor Joongheon Kim of the Korea University School of Electrical Engineering has co-authored a paper published in IEEE Communications Surveys & Tutorials, in collaboration with the research team of Associate Professor Soyi Jung of the Department of Electrical and Computer Engineering at Ajou University, researchers from the Electronics and Telecommunications Research Institute (ETRI), Professor Tony Q. S. Quek (IEEE Fellow) of the Singapore University of Technology and Design (SUTD), and Professor Zhu Han (IEEE Fellow) of the University of Houston. IEEE Communications Surveys & Tutorials currently has a Journal Impact Factor (IF) of 50.6, the highest among all IEEE journals and magazines.
The vision of sixth-generation (6G) mobile communications is to achieve global hyperconnectivity by integrating terrestrial, aerial, and maritime domains. Non-Terrestrial Networks (NTNs) are key enablers of this vision but face inherent challenges such as high mobility, long propagation delays, and limited onboard computational resources. Reinforcement Learning (RL) has emerged as a promising approach to addressing these challenges.
This paper presents a comprehensive tutorial on RL-based techniques for enabling seamless 6G NTN connectivity. It approaches the subject from two complementary perspectives: (i) examining how RL can address key NTN challenges such as handover, beamforming, and resource allocation, and (ii) exploring how the unique constraints of NTNs motivate the development of advanced RL techniques, including Quantum RL, Graph-based RL, and Federated RL.
Furthermore, based on the Third Generation Partnership Project (3GPP) Technical Report TR 38.811, the paper classifies the key characteristics of NTNs into four categories: Platform Dynamics, Altitude-Dependent Propagation, System Design Constraints, and Network Architecture. From these categories, the authors identify 14 optimization challenges. The paper also provides a comprehensive review of state-of-the-art RL-based approaches and examines their applicability and suitability for real-world NTN operations.
In addition, the paper discusses performance evaluation results of various techniques, future research directions, unresolved research challenges, and emerging technological paradigms that could drive the development of next-generation intelligent NTN systems. Ultimately, by bridging the gap between theoretical research and practical system implementation, this study highlights reinforcement learning as a foundational technology for realizing intelligent, adaptive, and resilient 6G NTN systems.
Further details about the paper are provided below.
Soyi Jung, Huiyeon Jang, Seoyeong Park, Junyoung Kim, Jiseok and Jang, In-Sop Cho, Minsu Shin, Tony Q. S. Quek, Zhu Han, and Joongheon Kim, “A Survey on Reinforcement Learning Methods for Seamless Connectivity in 6G Non-Terrestrial Networks: Algorithms and Key Findings,” IEEE Communications Surveys & Tutorials, vol. 28, pp. 7472-7511, October 2026. (DOI: 10.1109/COMST.2026.3740875)
The vision of sixth-generation (6G) mobile communications is to achieve global hyperconnectivity by integrating terrestrial, aerial, and maritime domains. Non-Terrestrial Networks (NTNs) are key enablers of this vision but face inherent challenges such as high mobility, long propagation delays, and limited onboard computational resources. Reinforcement Learning (RL) has emerged as a promising approach to addressing these challenges.
This paper presents a comprehensive tutorial on RL-based techniques for enabling seamless 6G NTN connectivity. It approaches the subject from two complementary perspectives: (i) examining how RL can address key NTN challenges such as handover, beamforming, and resource allocation, and (ii) exploring how the unique constraints of NTNs motivate the development of advanced RL techniques, including Quantum RL, Graph-based RL, and Federated RL.
Furthermore, based on the Third Generation Partnership Project (3GPP) Technical Report TR 38.811, the paper classifies the key characteristics of NTNs into four categories: Platform Dynamics, Altitude-Dependent Propagation, System Design Constraints, and Network Architecture. From these categories, the authors identify 14 optimization challenges. The paper also provides a comprehensive review of state-of-the-art RL-based approaches and examines their applicability and suitability for real-world NTN operations.
In addition, the paper discusses performance evaluation results of various techniques, future research directions, unresolved research challenges, and emerging technological paradigms that could drive the development of next-generation intelligent NTN systems. Ultimately, by bridging the gap between theoretical research and practical system implementation, this study highlights reinforcement learning as a foundational technology for realizing intelligent, adaptive, and resilient 6G NTN systems.
Further details about the paper are provided below.
Soyi Jung, Huiyeon Jang, Seoyeong Park, Junyoung Kim, Jiseok and Jang, In-Sop Cho, Minsu Shin, Tony Q. S. Quek, Zhu Han, and Joongheon Kim, “A Survey on Reinforcement Learning Methods for Seamless Connectivity in 6G Non-Terrestrial Networks: Algorithms and Key Findings,” IEEE Communications Surveys & Tutorials, vol. 28, pp. 7472-7511, October 2026. (DOI: 10.1109/COMST.2026.3740875)
File
Prev
2026.10.08
Next
No next post.
Engineering Building #412, College of Engineering, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, KoreaTEL : 02-3290-3210, 3211, 3213, 3214, 4592FAX : 02-921-0544
Copyrights ⓒ 2021 KOREA University. All Rights Reserved.
- Family Site