Near Optimal Online Resource Allocation Scheme for Energy Harvesting Cloud Radio Access Network with Battery Imperfections

  • Sijing Duan
  • Zhigang Chen
  • Deyu Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 768)


In energy harvesting wireless networks, the energy storage devices are usually imperfect. In this paper, we investigate dynamic online resource allocation scheme for Energy Harvesting Cloud Radio Access Network (EH-CRAN) by jointly considering the EH process, data admission, and a practical battery model with finite battery capacity, energy charging and discharging loss. We use Lyapunov optimization technique and design data queue and energy queue to formulate a stochastic optimization problem, and decompose the formulated problem into three subproblems, including data scheduling, power allocation and routing scheduling. Based on the solutions of these subproblems, an online resource allocation algorithm is proposed to maximize the user utility while ensuring the sustainability of RRHs. Furthermore, this algorithm does not require any prior statistical information of the system, e.g., channel state, data arrival and EH process. Both performance analysis and simulation results demonstrate the proposed algorithm can achieve close-to-optimal utility.


Cloud Radio Access Networks (C-RANs) Resource allocation optimization Energy harvesting (EH) Battery imperfections 



This work is supported by the National Natural Science Foundation of China (Grant No.71633006, Grant No. 61672540, Grant No. 61379057). This work is supported by The Fund of Postgraduate Student Independent Innovation Project of Central South University (2017zzzts625).


  1. 1.
    Demestichas, P., Georgakopoulos, A., Karvounas, D., Tsagkaris, K.: 5G on the horizon: key challenges for the radio-access network. IEEE Veh. Technol. Mag. 8(3), 47–53 (2013)CrossRefGoogle Scholar
  2. 2.
    Checko, A., Christiansen, H.L., Yan, Y., Scolari, L.: Cloud ran for mobile networksa technology overview. IEEE Commun. Surv. Tutor. 17(1), 405–426 (2015)CrossRefGoogle Scholar
  3. 3.
    Wang, X., Zhang, Y., Chen, T., Giannakis, G.B.: Dynamic energy management for smart-grid-powered coordinated multipoint systems. IEEE J. Sel. Areas Commun. 34(5), 1348–1359 (2016)CrossRefGoogle Scholar
  4. 4.
    Michelusi, N., Badia, L., Carli, R., Corradini, L.: Energy management policies for harvesting-based wireless sensor devices with battery degradation. IEEE Trans. Commun. 61(12), 4934–4947 (2013)CrossRefGoogle Scholar
  5. 5.
    Devillers, B., Gunduz, D.: A general framework for the optimization of energy harvesting communication systems with battery imperfections. J. Commun. Netw. 14(2), 130–139 (2012)CrossRefGoogle Scholar
  6. 6.
    Michelusi, N., Badia, L., Zorzi, M.: Optimal transmission policies for energy harvesting devices with limited state-of-charge knowledge. IEEE Trans. Commun. 62(11), 3969–3982 (2014)CrossRefGoogle Scholar
  7. 7.
    Tutuncuoglu, K., Yener, A., Ulukus, S.: Optimum policies for an energy harvesting transmitter under energy storage losses. IEEE J. Sel. Areas Commun. 33(3), 467–481 (2015)CrossRefGoogle Scholar
  8. 8.
    Biason, A., Zorzi, M.: Energy harvesting communication system with SOC-dependent energy storage losses (2016)Google Scholar
  9. 9.
    Ni, W., Dong, X.: Energy harvesting wireless communications with energy cooperation between transmitter and receiver. IEEE Trans. Commun. 63(4), 1457–1469 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Peng, M., Zhang, K., Jiang, J., Wang, J.: Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. IEEE Trans. Veh. Technol. 64(11), 5275–5287 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, K., Yang, K., Magurawalage, C.S.: Joint energy minimization and resource allocation in C-RAN with mobile cloud. IEEE Trans. Cloud Comput. 99(1) (2015)Google Scholar
  12. 12.
    Zhou, Z., Dong, M., Ota, K., Wang, G.: Energy-efficient resource allocation for D2D communications underlaying cloud-RAN-based LTE-A networks. IEEE Internet Things J. 3(3), 428–438 (2016)CrossRefGoogle Scholar
  13. 13.
    Sun, Y., Li, C., Huang, Y., Yang, L.: Energy-efficient resource allocation in C-RAN with fronthaul rate constraints. In: International Conference on Wireless Communications and Signal Processing, pp. 1–6 (2016)Google Scholar
  14. 14.
    Zeng, T., Zhen, M.A., Wang, G., Zhong, Z.: Green circuit design for battery-free sensors in cloud radio access network. China Commun. 12(11), 1–11 (2015)Google Scholar
  15. 15.
    Qiao, G., Leng, S., Zhang, Y., Zeng, M., Xu, L.: Multiple time-scale energy scheduling with energy harvesting aided heterogeneous cloud radio access networks. In: IEEE/CIC International Conference on Communications in China, pp. 1–6 (2016)Google Scholar
  16. 16.
    Biason, A., Zorzi, M.: On the effects of battery imperfections in an energy harvesting device, pp. 1–7 (2016)Google Scholar
  17. 17.
    Chalasani, S., Conrad, J.M.: A survey of energy harvesting sources for embedded systems. In: Southeastcon, pp. 442–447 (2008)Google Scholar
  18. 18.
    Mao, Z., Koksal, C.E., Shroff, N.B.: Near optimal power and rate control of multi-hop sensor networks with energy replenishment: basic limitations with finite energy and data storage. IEEE Trans. Autom. Control 57(4), 815–829 (2012)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Neely, M.J.: Energy optimal control for time-varying wireless networks. IEEE Trans. Inf. Theor. 52(7), 2915–2934 (2006)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Huang, L., Neely, M.J.: Utility optimal scheduling in energy-harvesting networks. IEEE/ACM Trans. Netw. 21(4), 1117–1130 (2013)CrossRefGoogle Scholar
  21. 21.
    Boyd, S., Vandenberghe, L., Faybusovich, L.: Convex optimization. IEEE Trans. Autom. Control 51(11), 1859–1859 (2006)CrossRefGoogle Scholar
  22. 22.
    Neely, M.: Stochastic network optimization with application to communication and queueing systems. Syn. Lect. Commun. Netw. 3(1), 211 (2010)MATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of SoftwareCentral South UniversityChangshaChina

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