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An Improved LBG Algorithm for User Clustering in Ultra-Dense Network

  • Yanxia LiangEmail author
  • Yao Liu
  • Changyin Sun
  • Xin Liu
  • Jing Jiang
  • Yan Gao
  • Yongbin Xie
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

A novel LBG-based user clustering algorithm is proposed to reduce interference efficiently in Ultra-Dense Network (UDN). There are two stages, weight design and user clustering. Because a user could interfere and be interfered by other users at the same time, a balanced cooperative transmission strategy is utilized in weight design. The improved LBG algorithm is used for user clustering, which overcomes the shortcoming of local optimum of conventional LBG. Moreover, this algorithm is superior to conventional LBG in computational complexity. Simulation results show that the sum rate of cell-edge users increases a lot compared to the reference algorithm, and the average system throughput gets higher obviously.

Keywords

Ultra-Dense Network Clustering Throughput Cell-edge user Interference 

Notes

Acknowledgement

This work was supported by National Science and Technology Major Project of the Ministry of Science and Technology of China (ZX201703001012-005), National Natural Science Foundation of China (61501371), Shaanxi STA International Cooperation and Exchanges Project (2017KW-011) and the Department of Education Shaanxi Province, China, under Grant 2013JK1023.

References

  1. 1.
    Yunas, S., Valkama, M., Niemela, J.: Spectral and energy efficiency of Ultra-Dense Networks under different eployment strategies. IEEE Commun. Mag. 53(1), 90–100 (2015)CrossRefGoogle Scholar
  2. 2.
    Wang, C., Hu, B., Chen, S., et al.: Joint dynamic access points grouping and resource allocation for coordinated transmission in user-centric UDN. Trans. Emerg. Telecommun. Technol. 29(3), e3265 (2017)CrossRefGoogle Scholar
  3. 3.
    Kunitaka, M., Tomoaki, O.: Orthogonal beamforming using Gram-Schmidt orthogonalization for downlink CoMP system. ITE Tech. Rep. 36(10), 17–20 (2012)Google Scholar
  4. 4.
    Bu, H.W., Xu, Y.H., Yuan, Z., Hu, Y.J., Yi, H.Y.: An efficient method for managing CoMP cooperating set based on central controller in LTE-A systems. Appl. Mech. Mater. 719–720, 721–726 (2015)CrossRefGoogle Scholar
  5. 5.
    Bassoy, S., Farooq, H., Imran, M.A., Imran, A.: Coordinated multi-point clustering schemes: a survey. IEEE Commun. Surv. Tutor. 19(2), 743–764 (2017)CrossRefGoogle Scholar
  6. 6.
    Grebla, G., Birand, B., van de Ven, P., Zussman, G.: Joint transmission in cellular networks with CoMP-stability and scheduling algorithms. Perform. Eval. 91(C), 38–55 (2015)CrossRefGoogle Scholar
  7. 7.
    Du, T., Qu, S., Liu, F., Wang, Q.: An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Inf. Fusion 21(1), 18–29 (2015)CrossRefGoogle Scholar
  8. 8.
    Xu, D., Ren, P., Du, Q., Sun, L.: Joint dynamic clustering and user scheduling for downlink cloud radio access network with limited feedback. China Commun. 12(12), 147–159 (2015)CrossRefGoogle Scholar
  9. 9.
    Ali, S.S., Saxena, N.: A novel static clustering approach for CoMP. In: IEEE 7th International Conference on Computing and Convergence Technology (ICCCT), Seoul, South Korea, pp. 757–762. IEEE Press (2012)Google Scholar
  10. 10.
    Wan, Q.: Research on multi-cell clustering cooperative technology in CoMP scene. Beijing University of Posts and Telecommunications, Beijing (2015)Google Scholar
  11. 11.
    Meng, N., Zhang, H.T., Lu, H.T.: Virtual cell-based mobility enhancement and performance evaluation in Ultra-Dense Networks. In: IEEE Wireless Communications and Networking Conference, Doha, Qatar, pp. 1–6. IEEE Press (2016)Google Scholar
  12. 12.
    Kurras, M., Fahse, S., Thiele, L.: Density based user clustering for wireless massive connectivity enabling Internet of Things. In: Globecom Workshops (GCWkshps), San Diego, CA, USA, pp. 1–6. IEEE Press (2015)Google Scholar
  13. 13.
    Patané, G., Russo, M.: The enhanced LBG algorithm. Neural Netw. Off. J. Int. Neural Netw. Soc. 14(9), 1219 (2001)CrossRefGoogle Scholar
  14. 14.
    Wang, J., Tang, S., Sun, C.: Resource allocation based on user clustering in ultra-dense small cell networks. J. Xi’an Univ. Posts Telecommun. 21(1), 16–20 (2016)Google Scholar
  15. 15.
    Gong, J., Zhou, S., Niu, Z., et al.: Joint scheduling and dynamic clustering in downlink cellular networks. In: Global Telecommunications Conference (Globecom), Houston, Texas, USA, pp. 1–5. IEEE Press (2011)Google Scholar
  16. 16.
    Ho, Z.K.M., Gesbert, D.: Balancing egoism and altruism on interference channel: the MIMO case. In: International Conference on Communications (ICC), Cape Town, South Africa, pp. 1–5. IEEE Press (2010)Google Scholar
  17. 17.
    Jindal, N., Rhee, W., Vishwanath, S., et al.: Sum power iterative water-filling for multi-antenna Gaussian broadcast channels. IEEE Trans. Inf. Theory 51(4), 1570–1580 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanxia Liang
    • 1
    Email author
  • Yao Liu
    • 2
  • Changyin Sun
    • 1
  • Xin Liu
    • 3
  • Jing Jiang
    • 1
  • Yan Gao
    • 1
  • Yongbin Xie
    • 1
  1. 1.Shaanxi Key Laboratory of Information Communication Network and SecurityXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  3. 3.School of Information EngineeringXi’an Eurasia UniversityXi’anChina

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