DOI QR코드

DOI QR Code

에너지 효율성 향상을 위한 DBSCAN 기반 기지국 모드 제어 알고리즘

DBSCAN-based Energy-Efficient Algorithm for Base Station Mode Control

  • Lee, Howon (Dept. of EECE, Hankyong National Univ.) ;
  • Lee, Wonseok (Standard Research Team, Innovative Technology Lab)
  • 투고 : 2019.10.31
  • 심사 : 2019.11.16
  • 발행 : 2019.12.31

초록

이동통신 시스템의 급격한 발전과 함께 다양한 모바일 융합서비스가 등장하고 있으며 이에 따른 데이터 트래픽도 폭발적으로 증가하고 있다. 이러한 급증하는 디바이스를 지원하기 위한 기지국의 수도 함께 증가하고 있기 때문에 통신사업자의 관점에서는 이러한 네트워크를 통해 소모되는 에너지 소모량을 줄이는 것이 매우 중요한 이슈 중 하나이다. 따라서 본 논문에서는 대표적인 사용자 밀집도 기반 클러스터링 기술 중 하나인 DBSCAN 알고리즘을 적용하여 사용자가 밀집된 영역을 추출하고 이렇게 추출된 서브네트워크 별로 씨닝 과정을 적용하여 기지국의 모드를 효율적으로 제어한다. 시뮬레이션을 통해 면적 당 수율과 에너지 효율 측면에서 제안 방안이 기존 방안 대비 높은 성능 결과를 가지는 것을 보인다.

With the rapid development of mobile communication systems, various mobile convergence services are appearing and data traffic is exploding accordingly. Because the number of base stations to support these surging devices is also increasing, from a network provider's point of view, reducing energy consumption through these mobile communication networks is one of the most important issues. Therefore, in this paper, we apply the DBSCAN (density-based spatial clustering of applications with noise) algorithm, one of the representative user-density based clustering algorithms, in order to extract the dense area with user density and apply the thinning process to each extracted sub-network to efficiently control the mode of the base stations. Extensive simulations show that the proposed algorithm has better performance results than the conventional algorithms with respect to area throughput and energy efficiency.

키워드

과제정보

This work was supported by a research grant from Hankyong National University for an academic exchange program in 2018.

참고문헌

  1. Rec. ITU-R M-2083-0, "IMT vision-framework and overall objectives of the future development of IMT for 2020 and beyond," ITU-R WP5D, Sep. 2015.
  2. J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, "What will 5G be?," IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1065-1082, Jun. 2014. https://doi.org/10.1109/JSAC.2014.2328098
  3. O. Queseth, D. Aziz, K. Kusume, H. Tullberg, M. Fallgren, M. Schellmann, M. Uusitalo, and M. Maternia, "ICT317669-METIS/D8.4 V1 METIS final project report," Tech. Rep., Apr. 2015. [Online]. Available: https://www.metis2020.com/wp-content/uploads/deliverables/METIS D8.4v1.pdf.
  4. J. Kim, and H. Lee, "D2D Based Advertisement Dissemination Using Expectation Maximization Clustering," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 5, pp. 992-998, May. 2017. https://doi.org/10.7840/kics.2017.42.5.992
  5. S. Tombaz, M. Usman, and J. Zander, "Energy efficiency improvements through heterogeneous networks in diverse traffic distribution scenarios," in Proceeding of the IEEE International Conference on Communications and Networking in China (CHINACOM), Aug. 2011.
  6. F. Richter, A. J. Fehske, and G. P. Fettweis, "Energy efficiency aspects of base station deployment strategies for cellular networks," in Proceeding of the IEEE Vehicular Technology Conference, Sep. 2009.
  7. D. Lopez-Perez, M. Ding, H. Claussen, and A. H. Jafari, "Towards 1 Gbps/UE in cellular systems: Understanding ultra-dense small cell deployments," IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2078-2101, Jun. 2015. https://doi.org/10.1109/COMST.2015.2439636
  8. Z. Hasan, H. Boostanimehr, and V. K. Bhargava, "Green cellular networks: A survey, some research issues and challenges," IEEE Commun. Surveys Tuts., vol. 13, no. 4, pp. 524-540, Aug. 2011. https://doi.org/10.1109/SURV.2011.092311.00031
  9. G. P. Koudouridis, H. Gao, and P. Legg, "A centralised approach to power on-off optimisation for heterogeneous networks" in Proceeding of the IEEE Vehicular Technology Conference, Sep. 2012.
  10. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, ''A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," in Proceeding of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 226-231, Aug. 1996.
  11. M. Haenggi, "Mean interference in hard-core wireless networks," IEEE Commun. Lett., vol. 15, pp. 792-794, Aug. 2011. https://doi.org/10.1109/LCOMM.2011.061611.110960
  12. W. Lee, B. C. Jung, and H. Lee, "ACEnet: Approximate thinning-based judicious network control for energyefficient ultra-dense networks," MDPI energies, vol.11, no. 5, pp. 1-11, May. 2018.