Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Learning-Backoff based Wireless Channel Access for Tactical Airborne Networks

차세대 공중전술네트워크를 위한 Learning-Backoff 기반 무선 채널 접속 방법

  • Byun, JungHun (Department of Computer Science, Chungbuk National University) ;
  • Park, Sangjun (Department of Electrical Engineering, Korea Military Academy) ;
  • Yoon, Joonhyeok (Department of Electrical Engineering, Korea Military Academy) ;
  • Kim, Yongchul (Department of Electrical Engineering, Korea Military Academy) ;
  • Lee, Wonwoo (Department of Electrical Engineering, Korea Military Academy) ;
  • Jo, Ohyun (Department of Computer Science, Chungbuk National University) ;
  • Joo, Taehwan (Agency for Defense Development(ADD))
  • 변정훈 (충북대학교 소프트웨어학과) ;
  • 박상준 (육군사관학교 전자공학과) ;
  • 윤준혁 (육군사관학교 전자공학과) ;
  • 김용철 (육군사관학교 전자공학과) ;
  • 이원우 (육군사관학교 전자공학과) ;
  • 조오현 (충북대학교 소프트웨어학과) ;
  • 주태환 (국방과학연구소)
  • Received : 2020.12.09
  • Accepted : 2021.01.20
  • Published : 2021.01.28
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Abstract

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

원활한 작전 수행을 통한 국방력의 강화를 위해 전술네트워크의 기능은 필수적이다. 전시 상황에서 다양한 전술, 전략은 수많은 정보들을 근거로 한다. 이를 위해 정찰기를 비롯한 다양한 정보 수집 장치 및 자원들이 방대한 양의 정보 수집을 위해 사용되고, 이들 대다수는 전술네트워크를 통해 정보를 전달한다. 채널의 사용 여부를 판단하여 상황에 따라 경쟁 기반으로 채널에 접속을 하는 국방전술네트워크 환경에서, 매우 높은 이동성을 갖는 정찰기 등 고속 이동 노드는 불필요한 채널 점유로 인하여 잠재적인 성능 열화 문제가 발생할 수 있다. 본 논문에서는 채널 예약 시점을 정하는 경쟁 윈도우(Contention Window)의 크기를 경험적으로 학습시켜 네트워크 처리량을 증가시키는 Learning-Backoff 방식의 무전 채널 접속 방법을 제안한다. 제안하는 방법은 고속 이동 노드의 수가 많아짐에 따라 더욱 좋은 성능을 보이고 있으며, 정찰기 4대가 운영되는 특정 작전 시나리오에 적용하였을 경우 처리량이 최대 25% 증가한다.

Keywords

Acknowledgement

This work was supported by Agency for Defense Development(ADD) under Grant(UD190011ED)

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