Joint User-Association and Resource-Allocation in Virtualized C-RAN

  • Xiaohong ZhangEmail author
  • Yong Li
  • Mugen Peng
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 211)


The Cloud Radio Access Network (C-RAN), which is a novel architecture, has been proposed as a promising solution to overcome the challenges of Next Generation (5G) cellular networks, in terms of efficiency, capacity, scalability, flexibility and sustainability in a cost-effective way. In this paper, we develop an efficient resource allocation scheme in the fronthaul-constrained C-RAN to support users of different slices (service providers). Multiple slices (service providers) share the resource of an InP, each slice has its own quality-of-service (QoS) requirement. In specific, we formulate an optimization problem for maximizing network throughput by joint subcarrier, power allocation and user-RRH association assignment in the downlink transmission of C-RAN. This problem is NP-hard, therefore, we introduce a two-step suboptimal algorithm to solve it. The original problem is decomposed into joint power and subcarrier allocation subproblem and user-RRH association assignment subproblem. Firstly, we solving the user-RRH association subproblem under the fronthaul capacity constraint by the binary search algorithm. Then the dual decomposition algorithm is used to solve the power and subcarrier allocation subproblem. Simulation results demonstrate the effectiveness of our proposed algorithm.


Virtualized C-RAN Fronthaul constrained Joint subcarrier Power and user-RRH association assignment 



This work was supported in part by the National High Technology Research and Development Program (863 Program) of China under Grant No. 2014AA01A707.


  1. 1.
    Checko, A., Christiansen, H.L., Yan, Y., et al.: Cloud RAN for mobile networks a technology overview. IEEE Commun. Surv. Tutorials 17(1), 405–426 (2015)CrossRefGoogle Scholar
  2. 2.
    Peng, M., Wang, C., et al.: Fronthaul-constrained cloud radio access networks: insights and challenges. IEEE Wirel. Commun. 22(2), 152–160 (2015)CrossRefGoogle Scholar
  3. 3.
    Huang, J., Duan, R., Cui, C., Chih-Lin, I.: Overview of cloud RAN. In: Proceedings of URSI General Assembly and Scientific Symposium (URSI GASS), Beijing, pp. 1–4, April 2014Google Scholar
  4. 4.
    Sun, Y., Li, C., Huang, Y., Yang, L.: Energy-efficient resource allocation in C-RAN with fronthaul rate constraints. In: Proceedings of International Conference on Wireless Communications Signal Processing (WCSP), Yangzhou, pp. 1–6, August 2016Google Scholar
  5. 5.
    Yoo, T.: Network slicing architecture for 5G network. In: Proceedings of International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, South Korea, pp. 1010–1014, October 2016Google Scholar
  6. 6.
    Pries, R., Morper, H.J., Galambosi, N., Jarschel, M.: Network as a service - a demo on 5G network slicing. In: Proceedings of International Teletraffic Congress (ITC), Wrzburg, Germany, pp. 209–211, September 2016Google Scholar
  7. 7.
    Parsaeefard, S., Dawadi, R., Derakhshani, M., Le-Ngoc, T.: Joint user-association and resource-allocation in virtualized wireless networks. IEEE Access 4, 2738–2750 (2016)CrossRefGoogle Scholar
  8. 8.
    Kamel, M.I., Le, L.B., Girard, A.: LTE wireless network virtualization: dynamic slicing via flexible scheduling. In: Proceedings of IEEE, Vehicular Technology Conference (VTC), Vancouver, BC, pp. 1–5, September 2014Google Scholar
  9. 9.
    Feng, Z., Qiu, C., et al.: An effective approach to 5G: wireless network virtualization. IEEE Commun. Mag. 53(12), 53–59 (2015)CrossRefGoogle Scholar
  10. 10.
    Niu, B., Zhou, Y., Shah-Mansouri, H., et al.: A dynamic resource sharing mechanism for cloud radio access networks. IEEE Trans. Wirel. Commun. 15(12), 8325–8338 (2016)CrossRefGoogle Scholar
  11. 11.
    Liu, L., Zhang, R.: Downlink SINR balancing in C-RAN under limited fronthaul capacity. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, pp. 3506–3510, May 2016Google Scholar
  12. 12.
    Kokku, R., Mahindra, R., Zhang, H., Rangarajan, S.: NVS: a substrate for virtualizing wireless resources in cellular networks. IEEE ACM Trans. Netw. 20(5), 1333–1346 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, L., Bi, S., Zhang, R.: Joint power control and fronthaul rate allocation for throughput maximization in OFDMA-based cloud radio access network. IEEE Trans. Commun. 63(11), 4097–4110 (2015)CrossRefGoogle Scholar
  14. 14.
    Fallgren, M.: An optimization approach to joint cell, channel and power allocation in multicell relay networks. IEEE Trans. Wirel. Commun. 11(8), 2868–2875 (2012)Google Scholar
  15. 15.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Yu, W., Liu, R.: Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Trans. Commun. 54(7), 1310–1322 (2006)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.The Key Laboratory of Universal Wireless Communication for Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations