Wireless Personal Communications

, Volume 104, Issue 2, pp 507–526 | Cite as

A QoS-Aware Joint Power and Subchannel Allocation Algorithm for Mobile Network Virtualization

  • Junyi WeiEmail author
  • Kun Yang
  • Guopeng Zhang
  • Xiaofeng Lu


Mobile network virtualization is a promising technology due to its flexibility and feasibility. Since it enables physical resources abstraction and sharing, the overall resource inefficiency can be reduced dramatically. By means of virtualization, mobile service providers can share their physical resources with multiple virtual network operators. In this paper, a joint power and subchannel allocation algorithm for mobile network virtualization (MNV) with quality of services support is proposed. It presents a resource allocation scheme for orthogonal frequency division multiple access-based MNV with multiple virtual network operators. An optimal solution is provided to maximize the total data rate of both infrastructure providers and virtual network operators. Numerical results have shown that the proposed resource allocation algorithm improves the overall performance.


Mobile network virtualization OFDMA Joint power and subchannel allocation QoS Optimal 



The work presented in this paper was partly funded by Natural Science Foundation of China (Grant No. 61572389), Zhongshan City Project (Grant No. 180809162197874) and National Nature Science Foundation of China (No. 61471361).


  1. 1.
    Wang, X., Krishnamurthy, P., & Tipper, D. (2013). Wireless network virtualization. In 2013 international conference on computing, networking and communications (ICNC) (pp. 818–822).Google Scholar
  2. 2.
    Gesbert, D., Hanly, S., Huang, H., Shamai Shitz, S., Simeone, O., & Yu, W. (2010). Multi-cell mimo cooperative networks: A new look at interference. IEEE Journal on Selected Areas in Communications, 28(9), 1380–1408.CrossRefGoogle Scholar
  3. 3.
    Lopez-Perez, D., Valcarce, A., de la Roche, G., & Zhang, J. (2009). Ofdma femtocells: A roadmap on interference avoidance. IEEE Communications Magazine, 47(9), 41–48.CrossRefGoogle Scholar
  4. 4.
    Mahindra, R., Bhanage, G., Hadjichristofi, G., Seskar, I., Raychaudhuri, D., Zhang, Y. (2008). Space versus time separation for wireless virtualization on an indoor grid. In Next generation internet networks, 2008. NGI 2008 (pp. 215–222).Google Scholar
  5. 5.
    Di Stasi, G., Avallone, S., & Canonico, R. (2013). Virtual network embedding in wireless mesh networks through reconfiguration of channels. In 2013 IEEE 9th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 537–544).Google Scholar
  6. 6.
    Lv, P., Cai, Z., Xu, J., & Xu, M. (2012). Multicast service-oriented virtual network embedding in wireless mesh networks. IEEE Communications Letters, 16(3), 375–377.CrossRefGoogle Scholar
  7. 7.
    Yun, D., & Yi, Y. (2011). Virtual network embedding in wireless multihop networks. In Proceedings of the 6th international conference on future internet technologies, ser. CFI ’11 (pp. 30–33). New York, NY: ACM.
  8. 8.
    Esposito, F., & Chiti, F. (2015). Distributed consensus-based auctions for wireless virtual network embedding. In 2015 IEEE international conference on communications (ICC) (pp. 472–477).Google Scholar
  9. 9.
    Abdelwahab, S., Hamdaoui, B., Guizani, M., & Znati, T. (2015). Efficient virtual network embedding with backtrack avoidance for dynamic wireless networks. IEEE Transactions on Wireless Communications, 99, 1.Google Scholar
  10. 10.
    Yang, M., Li, Y., Zeng, L., Jin, D., & Su, L. (2012). Karnaugh-map like online embedding algorithm of wireless virtualization. In 2012 15th international symposium on wireless personal multimedia communications (WPMC) (pp. 594–598).Google Scholar
  11. 11.
    Liang, C., & Yu, F. (2015). Wireless network virtualization: A survey, some research issues and challenges. IEEE Communications Surveys Tutorials, 17(1), 358–380.CrossRefGoogle Scholar
  12. 12.
    Kokku, R., Mahindra, R., Zhang, H., & Rangarajan, S. (2012). Nvs: A substrate for virtualizing wireless resources in cellular networks. IEEE/ACM Transactions on Networking, 20(5), 1333–1346.CrossRefGoogle Scholar
  13. 13.
    Bhanage, G., Seskar, I., Mahindra, R., & Raychaudhuri, D. (2010). Virtual basestation: Architecture for an open shared wimax framework. In Proceedings of the second ACM SIGCOMM workshop on virtualized infrastructure systems and architectures, ser. VISA ’10 (pp. 1–8). New York, NY: ACM.
  14. 14.
    Fu, F., & Kozat, U. C. (2013). Stochastic game for wireless network virtualization. IEEE/ACM Transaction on Networking, 21(1), 84–97. Scholar
  15. 15.
    Choi, Y., Kim, H., wook Han, S., & Han, Y. (2010). Joint resource allocation for parallel multi-radio access in heterogeneous wireless networks. IEEE Transactions on Wireless Communications, 9(11), 3324–3329.CrossRefGoogle Scholar
  16. 16.
    Miao, J., Hu, Z., Yang, K., Wang, C., & Tian, H. (2012). Joint power and bandwidth allocation algorithm with QoS support in heterogeneous wireless networks. IEEE Communications Letters, 16(4), 479–481.CrossRefGoogle Scholar
  17. 17.
    Zheng, J., Li, J., Shi, H., Liu, Q., & Yang, X. (2014). Joint subcarrier, code, and power allocation for parallel multi-radio access in heterogeneous wireless networks. Science China Information Sciences, 57(8), 1–5. Scholar
  18. 18.
    Liang, C., & Yu, F. (2015). Distributed resource allocation in virtualized wireless cellular networks based on ADMM. In 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 360–365).Google Scholar
  19. 19.
    Liang, C., & Yu, F. (2015). Virtual resource allocation in information-centric wireless virtual networks. In 2015 IEEE international conference on communications (ICC) (pp. 3915–3920).Google Scholar
  20. 20.
    Zhang, X., Li, Y., Jin, D., Su, L., Zeng, L., & Hui, P. (2012). Efficient resource allocation for wireless virtualization using time-space division. In 2012 8th international wireless communications and mobile computing conference (IWCMC) (pp. 59–64).Google Scholar
  21. 21.
    Checco, A., & Leith, D. (2015). Fair virtualization of 802.11 networks. IEEE/ACM Transactions on Networking, 23(1), 148–160.CrossRefGoogle Scholar
  22. 22.
    Zaki, Y., Zhao, L., Goerg, C., & Timm-Giel, A. (2010). LTE wireless virtualization and spectrum management. In Wireless and mobile networking conference (WMNC), 2010 Third Joint IFIP (pp. 1–6).Google Scholar
  23. 23.
    Liu, B., & Tian, H. (2013). A bankruptcy game-based resource allocation approach among virtual mobile operators. IEEE Communications Letters, 17(7), 1420–1423.CrossRefGoogle Scholar
  24. 24.
    Lu, X., Yang, K., Liu, Y., Zhou, D., & Liu, S. (2012). An elastic resource allocation algorithm enabling wireless network virtualization. Wireless Communications and Mobile Computing, 15(2), 295–308. Scholar
  25. 25.
    Ng, D. W. K., Lo, E. S., & Schober, R. (2013). Energy-efficient resource allocation in multiuser OFDM systems with wireless information and power transfer. In 2013 IEEE Wireless communications and networking conference (WCNC) (pp. 3823–3828). IEEE.Google Scholar
  26. 26.
    Shi, Q., Xu, W., Li, D., Wang, Y., Gu, X., & Li, W. (2013). On the energy efficiency optimality of OFDMA for SISO-OFDM downlink system. IEEE Communications Letters, 17(3), 541–544.CrossRefGoogle Scholar
  27. 27.
    Xu, C., Sheng, M., Yang, C., Wang, X., & Wang, L. (2013). Pricing based multi-resource allocation in OFDMA cognitive radio networks: an energy efficiency perspective.Google Scholar
  28. 28.
    Badic, B., O’Farrrell, T., Loskot, P., & He, J. (Sept 2009). Energy efficient radio access architectures for green radio: Large versus small cell size deployment. In 2009 IEEE 70th vehicular technology conference fall (VTC 2009-Fall) (pp. 1–5).Google Scholar
  29. 29.
    Kim, Y., Miao, G., & Hwang, T. (2014). Energy efficient pilot and link adaptation for mobile users in tdd multi-user mimo systems. IEEE Transactions on Wireless Communications, 13(1), 382–393.CrossRefGoogle Scholar
  30. 30.
    Xiao, X., Tao, X., & Lu, J. (2014). QoS-guaranteed energy-efficient power allocation in downlink multi-user MIMO-OFDM systems. In 2014 IEEE international conference on communications (ICC) (pp. 3945–3950).Google Scholar
  31. 31.
    Xu, Z., Yang, C., Li, G., Zhang, S., Chen, Y., & Xu, S. (2013). Energy-efficient configuration of spatial and frequency resources in MIMO–OFDMA systems. IEEE Transactions on Communications, 61(2), 564–575.CrossRefGoogle Scholar
  32. 32.
    Abraham, S., & Popescu, D. C. (2013). Joint transmitter adaptation and power control for cognitive radio networks with target sir requirements. Physical Communication, 9, 223–230.CrossRefGoogle Scholar
  33. 33.
    Wolsey, L. A., & Nemhauser, G. L. (2014). Integer and combinatorial optimization. London: Wiley.zbMATHGoogle Scholar
  34. 34.
    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  2. 2.China University of Mining and TechnologyXuzhouChina
  3. 3.ISN LabXidian UniversityXi’anChina

Personalised recommendations