Advertisement

QvHran: A QoE-Driven Virtualization Based Architecture for Heterogeneous Radio Access Network

  • Luhan WangEmail author
  • Zhaoming Lu
  • Xiangming Wen
  • Lu Ma
  • Xin Chen
  • Wei Zheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)

Abstract

Heterogeneous cloud RANs have been proposed as a cost-effective solution to promote wireless network coverage and data rate. However, many urgent issues, such as cross-tier interference avoidance, converged network management and the fulfillment of consistent experience quality, still need to be tackled. SDN and virtualization empower the high efficiency management of networks, based on which, a QoE driven RAN architecture (QvHran) is proposed. QvHran aims at providing a new organization and management norm to perform consistent management for future H-CRANS. We design the QvHran architecture from both management and deployment aspect, and also study the relationships between them. Based on the proposed architecture, its key supporting technologies are also presented, include heterogeneous resource virtualization, network situation awareness, and elastic allocation of virtual resources. Lastly, a simulation is given to demonstrate the elastic resource allocation in QvHran. Simulation results has shown that RAN performance can be improved a lot in QvHran.

Keywords

HetNets SDN Virtualization RAN architecture QoE 

Notes

Acknowledgement

This work is supported by Beijing Advanced Innovation Center for Future Internet Technology, and Beijing Municipal Science and technology Commission research fund project No. D151100000115002.

References

  1. 1.
    Cisco. Cisco Visual Network Index: Global Mobile Data Traffic Forecast Update, 2014–2019 (2015)Google Scholar
  2. 2.
    Yong Sheng, S., et al.: Energy efficient heterogeneous cellular networks. Sel. Areas Commun. IEEE J. 31(5), 840–850 (2013)CrossRefGoogle Scholar
  3. 3.
    Peng, M., Yuan, L., Jiang, J., Li, J., Wang, C.: Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies (2014)Google Scholar
  4. 4.
    ONF: Software-Defined Networking: The New Norm for Networks. ONF White Paper (2012)Google Scholar
  5. 5.
    Anjing, W., et al.: Network virtualization: technologies, perspectives, and frontiers. J. Lightwave Technol. 31(4), 523–537 (2013)CrossRefGoogle Scholar
  6. 6.
    Sheng, Z., et al.: CHORUS: a framework for scalable collaboration in heterogeneous networks with cognitive synergy. IEEE Wirel. Commun. 20(4), 133–139 (2013)CrossRefGoogle Scholar
  7. 7.
    Ali-Ahmad, H., et al.: CROWD: an SDN approach for DenseNets. In: 2013 Second European Workshop on Software Defined Networks (EWSDN) (2013)Google Scholar
  8. 8.
    Gudipati, A., et al.: SoftRAN: software defined radio access network. In: Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, ACM (2013)Google Scholar
  9. 9.
    Bansal, M., et al.: Openradio: a programmable wireless dataplane. In: ACM Proceedings of the First Workshop on Hot Topics in software Defined Networks (2012)Google Scholar
  10. 10.
    Wen, H., Tiwary, P.K., Le-Ngoc, T.: Current trends and perspectives in wireless virtualization. In: 2013 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT), IEEE (2013)Google Scholar
  11. 11.
    Kokku, R., et al.: NVS: a substrate for virtualizing wireless resources in cellular networks. IEEE/ACM Trans. Netw. 20(5), 1333–1346 (2012)CrossRefGoogle Scholar
  12. 12.
    Bernardos, C., et al.: An architecture for software defined wireless networking. IEEE Wirel. Commun. 21(3), 52–61 (2014)CrossRefGoogle Scholar
  13. 13.
    Wang, L., et al.: Open wireless network architecture in radio access network. In: 2013 IEEE 78th Vehicular Technology Conference (VTC Fall) (2013)Google Scholar
  14. 14.
    Fattah, H.: Analysis of the channel access mechanism in IEEE 802.11 wireless local area networks. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PacRim 2007 (2007)Google Scholar
  15. 15.
    Zhao, X., Lu, Z., Wang, L., Wen, X., Lei, T.: Service-oriented network performance evaluation framework based on LA-FAHP. J. China Univ. Posts Telecom V22(3), 74–83 (2015)Google Scholar
  16. 16.
    Xia, X., et al.: Blind video quality assessment using natural video spatio-temporal statistics. In: 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6 (2014)Google Scholar
  17. 17.
    Guvenc, I., et al.: Capacity and fairness analysis of heterogeneous networks with range expansion and interference coordination. IEEE Commun. Lett. 15(10), 1084–1087 (2011)CrossRefGoogle Scholar
  18. 18.
    Misevičius, A.: A modified simulated annealing algorithm for the quadratic assignment problem. Informatica 14(4), 497–514 (2003)MathSciNetGoogle Scholar
  19. 19.
    Banani, S.A., Eckford, A., Adve, R.: Analyzing dependent placements of small cells in a two-layer heterogeneous network with a rate coverage constraint. IEEE Trans. Veh. Technol. 65, 9801–9816 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Luhan Wang
    • 1
    • 2
    Email author
  • Zhaoming Lu
    • 1
    • 2
  • Xiangming Wen
    • 2
  • Lu Ma
    • 2
  • Xin Chen
    • 2
  • Wei Zheng
    • 2
  1. 1.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations