Corbin Kim

Corbin Kim

Ph.D. Candidate, NC State University

Ph.D. Candidate at NextG Lab, NC State University (ECE). My research focuses on applying Large Language Models to automate wireless resource management in Radio Access Networks (RAN), working toward fully autonomous Zero Touch Networks. I actively contribute to the AIMLFW project within the O-RAN Software Community.

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Location
NextG Lab, ECE Department, 27695, Raleigh, North Carolina, United States
Email
Website
https://geonkim.netlify.app/
LinkedIn
Corbin Kim

Experience

–

Graduate Research Assistant at Kyung Hee University

Convergence Engineering Institute for Emerging Communication Technology, Department of Electronics and Information. Adviser: Prof. EenKee Hong.

Highlights

  • O-RAN Global PlugFest 2024: Collaborated with LG Uplus to validate Open RAN interoperability
  • Open RAN Workforce Development: Registered 'Traffic Forecasting for Green Network' use case in O-RAN SC AIMLFW
  • 5G NR Open Small Cell: Improved energy efficiency and traffic prediction using Open RAN-based small cells
  • 5G Open RAN Network Slicing: Developed RAN slicing technologies for service-level assurance (with Prof. Tony Q.S. Quek)
  • SKT AI Fellowship: Developed cell-off recommendation using SKT's commercial network data, measured ROI at Busan testbed

Education

– present

Ph.D. in Electrical and Computer Engineering from North Carolina State University with GPA of

–

M.S. in Mobile Communication from Kyung Hee University with GPA of 3.94

Courses

  • Thesis: Green Intelligence - Traffic Forecasting for Cell Sleeping in Open RAN

–

B.S. in Electronic Engineering from Kyung Hee University with GPA of

Awards

Best Paper Award from Korean Institute of Communications and Information Sciences

Outstanding research on O-RAN testbed implementation

Outstanding Research Award from SKT

5G Green AI Algorithm research project

Best Paper Award from Korean Institute of Communications and Information Sciences

Outstanding research on traffic prediction

Publications

Traffic Prediction for Carbon Reduction in Green Cellular Networks by IEEE Trans. Green Commun. Netw. (in preparation)

G. Kim, S.J. Lee and E.K. Hong

Implementation of Ultra-Low Latency Open RAN Using Distributed Near-RT RIC by Proc. KICS Symposium

G. Kim, S.J. Lee and E.K. Hong, Gangwon, Jan 2024

Securing 5G Core Network Stability Using Open Source by Proc. KICS Symposium

G. Kim, S.Y. Lee, S.J. Lee and E.K. Hong, Gyeongbuk, Nov 2023

Cell Free Massive MIMO Downlink Power Allocation Using Deep Reinforcement Learning by Proc. KICS Symposium

G. Kim and E.K. Hong, Gangwon, Jun 2023

Languages

Korean
Fluency: Native speaker
English
Fluency: Professional working proficiency

Skills

AI/ML for Wireless Networks
Level: Master
Keywords:
  • AI/ML
  • Reinforcement Learning
  • Traffic Prediction
  • Optimization Theory
Network Systems
Level: Intermediate
Keywords:
  • O-RAN
  • 5G/6G
  • RAN Slicing
  • Network Slicing
Programming
Level: Master
Keywords:
  • Python
  • Go
  • C/C++
  • Kubernetes (CKA)

Interests

Research
Keywords:
  • Zero Touch Networks
  • Agentic AI
  • Autonomous RAN