Wireless Networks

, Volume 25, Issue 6, pp 3665–3674 | Cite as

New dual-game-based cooperative bandwidth control scheme for ultra-dense networks

  • Sungwook KimEmail author


Future 5G cellular networks are being designed to address the explosive traffic growth of mobile users. In emerging new wireless system paradigms, the ultra-dense network (UDN) is considered to be one of the key technologies for potentially achieving the next generation network capacity. For UDN operations, the main challenge is to design efficient bandwidth resource allocation algorithms while considering quality-of-service provisioning. In this paper, we present an intelligent UDN bandwidth control scheme using efficient and innovative methodologies. Based on the basic ideas of cooperative game theory, the proposed scheme can adaptively assign the limited bandwidth resource for each small cell operator. By employing two different game solutions, the proposed approach is designed as a novel dual-game model. First, individual cell operators estimate their bandwidth requirements according to the iterative Nash bargaining solution. Then, limited bandwidth is distributed to each cell operator based on the τ-value. To reduce the computation complexity, the proposed algorithms are hierarchically implemented with cascade interactions. Through simulation results, we confirm that system throughput, bandwidth utilization, and load balancing among the cells can be improved with the proposed approach compared to the existing schemes. Finally, we propose further challenges and opportunities in the research area of UDN operations.


Ultra-dense networks Cooperative game theory τ-Value Nash bargaining solution Bandwidth allocation 



This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2018-0-01799) supervised by the IITP (Institute for Information and communications Technology Promotion) and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A09081759).


  1. 1.
    Chen, M., & Hao, Y. (2018). Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications, 36(3), 587–597.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cisco Visual Networking Index: Global mobile data traffic forecast update 20162021 white paper, Feb. 2017.
  3. 3.
    Shi, Y., Zhang, J., Chen, W., & Letaief, K. B. (2018). Generalized sparse and low-rank optimization for ultra-dense networks. IEEE Communications Magazine, 56(6), 42–48.CrossRefGoogle Scholar
  4. 4.
    Kamel, M., Hamouda, W., & Youssef, A. (2016). Ultra-dense networks: a survey. IEEE Communications Surveys & Tutorials, 18(4), 2522–2545.CrossRefGoogle Scholar
  5. 5.
    Marabissi, D., Bartoli, G., Fantacci, R., & Micciullo, L. (2018). Energy efficient cooperative multicast beamforming in ultra dense networks. IET Communications, 12(5), 573–578.CrossRefGoogle Scholar
  6. 6.
    Uno, K. K. S., & Kim, M. (2010). Adaptive QoS mechanism for wireless mobile network. JCSE, 4(2), 153–172.CrossRefGoogle Scholar
  7. 7.
    Pande, A., Ramamurthi, V., & Mohapatra, P. (2013). Quality-oriented video delivery over LTE. JCSE, 7(3), 168–176.CrossRefGoogle Scholar
  8. 8.
    Jang, I., Pyeon, D., Kim, S., & Yoon, H. (2013). A survey on communication protocols for wireless sensor networks. JCSE, 7(4), 231–241.CrossRefGoogle Scholar
  9. 9.
    Guanding, Yu., Liu, R., Chen, Q., & Tang, Z. (2018). A hierarchical SDN architecture for ultra-dense millimeter-wave cellular networks. IEEE Communications Magazine, 56(6), 79–85.CrossRefGoogle Scholar
  10. 10.
    Rizvi, S., Karpinski, K., & Razaque, A. (2015). Novel architecture of self-organized mobile wireless sensor networks. JCSE, 9(4), 163–176.CrossRefGoogle Scholar
  11. 11.
    Kim, S. (2014). Game theory applications in network design. Hershey, PA: IGI Global.CrossRefGoogle Scholar
  12. 12.
    Zhang, H., Huang, S., Jiang, C., Long, K., Leung, Victor C. M., & Vincent Poor, H. (2017). Energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947.CrossRefGoogle Scholar
  13. 13.
    Zhang, H., Dong, Y., Julian Cheng, M., Hossain, J., & Leung, Victor C. M. (2016). Fronthauling for 5G LTE-U ultra dense cloud small cell networks. IEEE Wireless Communications, 23(6), 48–53.CrossRefGoogle Scholar
  14. 14.
    Zhang, H., Jiang, C., Beaulieu, N. C., Chu, X., Wang, X., & Quek, Tony Q. S. (2015). Resource allocation for cognitive small cell networks: a cooperative bargaining game theoretic approach. IEEE Transactions on Wireless Communications, 14(6), 3481–3493.CrossRefGoogle Scholar
  15. 15.
    Qiu, J., Ding, G., Qihui, W., Qian, Z., Tsiftsis, T. A., Zhiyong, D., et al. (2016). Hierarchical resource allocation framework for hyper-dense small cell networks. IEEE Access, 4, 8657–8669.CrossRefGoogle Scholar
  16. 16.
    Li, W., & Zhang, J. (2018). Cluster-based resource allocation scheme with QoS guarantee in ultra-dense networks. IET Communications, 12(7), 861–867.CrossRefGoogle Scholar
  17. 17.
    Liu, Y., Wang, Y., Zhang, Y., Sun, R., & Jiang, L. (2016). Game-theoretic hierarchical resource allocation in ultra-dense networks. In IEEE PIMRC’2016 (pp. 1–6).Google Scholar
  18. 18.
    Tijs, S. H. (1987). An axiomatization of the τ-value. Mathematical Social Sciences, 13(2), 177–181.MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Yanovskaya, E. B. (2010). The nucleolus and the τ-value of interval games. Contributions to Game Theory and Management, 3, 421–430.MathSciNetzbMATHGoogle Scholar
  20. 20.
    Nuñez, M., & Rafels, C. (2002). The assignment game: the τ-value. International journal of Game Theory, 31, 411–422.MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Kim, J. Y., Bang, I., Sung, D. K., Yi, Y., & Kim, B.-H. (2015). Design of a multi-variable QoE function based on the remaining battery energy. In IEEE PIMRC’2015 (pp. 976–980).Google Scholar
  22. 22.
    Benkacem, I., Taleb, T., Bagaa, M., & Flinck, H. (2018). Optimal VNFs placement in CDN slicing over multi-cloud environment. IEEE Journal on Selected Areas in Communications, 36(3), 616–627.CrossRefGoogle Scholar
  23. 23.
    Branzei, R., Dimitrov, D., & Tijs, S. (2005). Models in cooperative game theory: crisp, fuzzy and multichoice games. Berlin: Springer.zbMATHGoogle Scholar
  24. 24.
    Jia, Y., Tian, H., Fan, S., Zhao, P., & Zhao, K. (2018). Bankruptcy game based resource allocation algorithm for 5G Cloud-RAN slicing. In IEEE WCNC’2018 (pp. 1–6).Google Scholar
  25. 25.
    Niyato, D., & Hossain, E. (2006). A cooperative game framework for bandwidth allocation in 4G heterogeneous wireless networks. In IEEE international conference on communications (pp. 4357–4362).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSogang UniversitySeoulSouth Korea

Personalised recommendations