DOI QR코드

DOI QR Code

차세대 공중전술네트워크를 위한 Learning-Backoff 기반 무선 채널 접속 방법

Learning-Backoff based Wireless Channel Access for Tactical Airborne Networks

  • 변정훈 (충북대학교 소프트웨어학과) ;
  • 박상준 (육군사관학교 전자공학과) ;
  • 윤준혁 (육군사관학교 전자공학과) ;
  • 김용철 (육군사관학교 전자공학과) ;
  • 이원우 (육군사관학교 전자공학과) ;
  • 조오현 (충북대학교 소프트웨어학과) ;
  • 주태환 (국방과학연구소)
  • 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))
  • 투고 : 2020.12.09
  • 심사 : 2021.01.20
  • 발행 : 2021.01.28

초록

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.

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.

키워드

과제정보

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

참고문헌

  1. G. Bianchi, L. Fratta & M. Oliveri. (1996, October). Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs. In Proceedings of PIMRC'96-7th International Symposium on Personal, Indoor, and Mobile Communications (Vol. 2, pp. 392-396). IEEE. DOI : 10.1109/PIMRC.1996.567423
  2. C. J. Watkins & P. Dayan. (1992). Q-learning. Machine learning, 8(3-4), 279-292. https://doi.org/10.1007/BF00992698
  3. R. S. Sutton & A. G. Barto. (2018). Reinforcement learning: An introduction. MIT press.
  4. S. H. Park & O. Jo. (2020). Q-NAV: NAV Setting Method based on Reinforcement Learning in Underwater Wireless Networks. arXiv preprint arXiv:2005.13521.
  5. S. H. Park, K. Shin & O. Jo. (2020). AQ-NAV: Reinforced Learning Based Channel Access Method Using Distance Estimation in Underwater Communication. Journal of Convergence for Information Technology, 10(7), 33-40. DOI : 10.22156/CS4SMB.2020.10.07.033
  6. S. Galzarano, A. Liotta & G. Fortino. (2013, December). QL-MAC: A Q-learning based MAC for wireless sensor networks. In International Conference on Algorithms and Architectures for Parallel Processing (pp. 267-275). Springer, Cham.
  7. N. Aihara, K. Adachi, O. Takyu, M. Ohta & T. Fujii. (2019). Q-learning aided resource allocation and environment recognition in LoRaWAN with CSMA/CA. IEEE Access, 7, 152126-152137. DOI : 10.1109/ACCESS.2019.2948111
  8. S. Bao & T. Fujii. (2011, November). Q-learning based p-pesistent csma mac protcol for secondary user of cognitive radio networks. In 2011 Third International Conference on Intelligent Networking and Collaborative Systems (pp. 336-337). IEEE. DOI : 10.1109/INCoS.2011.140
  9. S. Cho. (2020). Rate adaptation with Q-learning in CSMA/CA wireless networks. Journal of Information Processing Systems, 16(5), 1048-1063. DOI : 10.3745/JIPS.03.0148
  10. S. Hayat, E. Yanmaz & R. Muzaffar. (2016). Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Communications Surveys & Tutorials, 18(4), 2624-2661. DOI : 10.1109/COMST.2016.2560343
  11. C. I. Yeo, Y. S. Heo, J. H. Ryu, S. W. Park, S. C. Kim, H. S. Kang & G. H. Lee. (2020). Recent R&D Trends in Wireless Network Technology based on UAV-assisted FSO Technique. [ETRI] Electronics and Telecommunications Trends, 35(2), 38-49.
  12. W. Fawaz, C. Abou-Rjeily & C. Assi. (2018). UAV-aided cooperation for FSO communication systems. IEEE Communications Magazine, 56(1), 70-75. DOI : 10.1109/MCOM.2017.1700320
  13. B. Moision et al. (2017, February). Demonstration of free-space optical communication for long-range data links between balloons on Project Loon. In Free-Space Laser Communication and Atmospheric Propagation XXIX (Vol. 10096, p. 100960Z). International Society for Optics and Photonics.
  14. P. Wolfowitz. (2002). Global Information Grid (GIG) Overarching Policy. US Department of Defense, directive, (8100.1).
  15. R. Trafton & S. V. Pizzi. (2006, October). The joint airborne network services suite. In MILCOM 2006-2006 IEEE Military Communications conference (pp. 1-5). IEEE. DOI : 10.1109/MILCOM.2006.302496