Intelligent Wireless Networks Lab.

@ Kumoh National Institute of Technology

금오공과대학교 전자공학부 지능형 무선 네트워크 연구실(IWNL)에서는 열정있는 학부연구생과 대학원생을 모집합니다.

관심있는 분들은 hc.yang@kumoh.ac.kr로 간단한 자기 소개를 담아 연락 바랍니다.

We are now recruiting passionate and self-motivated undergraduate and graduate students on next generation communication systems and distributed networks for data storage and machine learning. If you are interested, please contact us with a brief self-introduction to hc.yang@kumoh.ac.kr.

About Us

우리 연구실에서는 무선 통신 시스템 또는 데이터 무선 네트워크에 대한 전반적인 연구를 수행하고 있습니다. 특히 사물 인터넷 (IoT) 환경의 방대한 데이터 처리 수요에 발맞춰 분산형 데이터 네트워크에서의 데이터 통신, 저장, 계산을 위한 새로운 무선 네트워크 설계에 관한 원천 기술을 연구하고 있습니다.

주요 연구분야

  • 수많은 센서, 노드로 이루어진 IoT 네트워크에서의 무선 통신을 위한 간섭 처리, 다중 접속 기술

  • 데이터 분산 저장 시스템에서의 데이터 보안, 사용자 프라이버시를 보장하는 데이터 인코딩 기법

  • 분산 컴퓨팅 시스템의 성능 향상을 위한 데이터 인코딩 기법 및 업무 할당 알고리즘

We are conducting an overall study of wireless communication systems and wireless data networks. In particular, in response to the vast demands of data processing in the Internet of Things (IoT) environment, we are researching fundamental technologies for designing wireless networks for data communication, storage, and computation in distributed networks.

Main research field

  • Interference management and multiple access technologies for wireless communication in IoT networks consisting of numerous sensors and nodes

  • Data encoding techniques to ensure data security and user privacy in distributed data storage

  • Data encoding techniques and task allocation algorithms to improve the performance of distributed computing systems

Latest News

  • Our group is funded by the Grand ICT Research Center from IITP since Jan. 2021.

  • The paper entitled "Fully Private Coded Matrix Multiplication from Colluding Workers," has been accepted to IEEE Communications Letters in Nov. 2020.

  • Our group is funded by the four-stage BK21 (Brain Korea 21) project of Ministry of Education since Sept. 2020.

  • The paper entitled "Squeezed Polynomial Codes: Communication-Efficient Coded Computation in Straggler-Exploiting Distributed Matrix Multiplication," has been accepted to IEEE Access in Oct. 2020.

  • The paper entitled "On the Design of Tailored Neural Networks for Energy Harvesting Broadcast Channels: A Reinforcement Learning Approach," has been accepted to IEEE Access in Sept. 2020.

  • Heecheol Yang is serving for IEEE WCNC 2021 as a TPC member.

  • The paper entitled "Repair of Multiple Descriptions on Distributed Storage," has been accepted to International Symposium on Information Theory and Its Applications (ISITA) in Oct. 2020.

  • The paper entitled "RL-based Transmission Completion Time Minimization with Energy Harvesting for Time-varying Channels," has been accepted to IEEE International Conference on Communications (ICC) Workshop on Machine Learning in Communications in Jun. 2020.

  • Heecheol Yang is funded by the National Research Foundation of Korea grant for 2 years since Mar. 2020.

  • Heecheol Yang is serving for IEEE WCNC 2020 as a TPC member.

  • The paper entitled "Private Coded Matrix Multiplication," has been accepted to IEEE Transactions on Information Forensics and Security in Sept. 2019.

Sponsors

Contact Us

  • Tel.: +82-54-478-7507

  • E-mail: hc.yang@kumoh.ac.kr

  • Address: Rm. 604, Bd. Digital-Gwan, 61 Daehak-ro, Gumi, Gyungbuk, 39177, Korea