Wireless Networks

, Volume 25, Issue 8, pp 4555–4567 | Cite as

Characterization of sparse beamforming for energy efficiency in cloud radio access networks using Gauss–Poisson process

  • Majid FarahmandEmail author
  • Abbas Mohammadi


In this paper, we propose an energy efficient Sparse Beamforming Strategy (SBS) in Cloud Radio Access Networks (C-RAN) to achieve an optimimum energy efficiency. The stochastic geometry method is used to derive some expressions for ergodic rate and coverage probability in downlink transmission. In this system model, Remote Radio Heads (RRHs) are coordinated by a Baseband Unit to transmit data toward users. We assume using RRH clusters where each cluster includes one or two RRHs. To investigate this system, we have used Gauss–Poisson process (GPP). The GPP well describes this clustering scenarios. Considering the intra-cell interference power control, we propose SBS to gain the best performance subject to the energy efficiency (EE) metric. SBS which is based on sparse selections of RRHs, is characterized by introducing analytical expressions and simulation of a C-RAN scenario which the allocation of the RRHs follows the GPP distribution. The numerical results demonstrate that the proposed SBS method improves the overall EE of C-RAN scenario up to 15% with respect to full RRHs coordination, at the high intra-cell interference conditions, and about 30% with respect to no coordination state between RRHs in low intra-cell interference regime.


Cloud radio access networks Gauss–Poisson process Sparse beamforming Energy efficiency 


  1. 1.
    Cheng, M., Wang, J. B., Wu, Y., & Lin, M. (2017). Downlink ergodic rate analysis for virtual cell based cloud radio access networks. IEEE Access Journal, 5, 13520–13531.CrossRefGoogle Scholar
  2. 2.
    Wu, J., Zhang, Zh, Hong, Y., et al. (2015). Cloud radio access network (C-RAN): A primer. IEEE Network, 29, 35–41.CrossRefGoogle Scholar
  3. 3.
    Douik, A., Dahrouj, H., Al-Naffouri, T., et al. (2016). Coordinated scheduling and power control in cloud-radio access networks. IEEE Transactions on Wireless Communications, 15, 2523–2536.CrossRefGoogle Scholar
  4. 4.
    Fakhri, Z. H., Khan, M., & Sabir, F., et al. (2017). A resource allocation mechanism for cloud radio access network based on cell differentiation and integration concept. IEEE Transactions on Network Science and Engineering. Scholar
  5. 5.
    Li, Y., Celebi, H., & Daneshmand, M. (2013). Energy efficient femtocell networks: Challenges and opportunities. IEEE Wireless Communications, 20(6), 99–105.CrossRefGoogle Scholar
  6. 6.
    Chih-lin, I., Yuan, Y., Huang, J., et al. (2015). Rethink fronthaul for soft RAN. IEEE Communications Magazine, 53(9), 82–88.CrossRefGoogle Scholar
  7. 7.
    Marsch, P., & Fettweis, G. P. (2011). Coordinated multi-point in mobile communications: From theory to practice. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  8. 8.
    Tolli, A., Pennanen, H., & Komulainen, P. (2011). Decentralized minimum power multi-cell beam-forming with limited backhaul signaling. IEEE Transactions on Wireless Communications, 10, 570–580.CrossRefGoogle Scholar
  9. 9.
    Li, Y., Liao, C., Wang, Y., & Wang, Ch. (2015). Energy-efficient optimal relay selection in cooperative cellular networks based on double auction. IEEE Transactions on Wireless Communications, 14(8), 4093–4104.CrossRefGoogle Scholar
  10. 10.
    Farahmand, M., & Mohammadi, A. (2017). Sparse power allocation in downlink transmission of cloud radio access networks. IET Communications, 11(16), 2531–2538.CrossRefGoogle Scholar
  11. 11.
    Dai, B., & Yu, W. (2014). Sparse beamforming and user-centric clustering for downlink cloud radio access network. IEEE Access, 2, 1326–1339.CrossRefGoogle Scholar
  12. 12.
    Li, Y., Zhu, X., Liao, Ch., et al. (2015). Energy efficiency maximization by jointly optimizing the positions and serving range of relay stations in cellular networks. IEEE Transactions on Vehicular Technology, 64(6), 2551–2560.CrossRefGoogle Scholar
  13. 13.
    Li, J., Wu, J., Peng, M., et al. (2016). Queue-aware energy-efficient joint remote radio head activation and beamforming in cloud radio access networks. IEEE Transactions on Wireless Communications, 15(6), 3380–3894.CrossRefGoogle Scholar
  14. 14.
    Shi, Y., Zhang, J., & Letaief, Kh B. (2014). Robust group sparse beamforming for multicast green cloud-RAN with imperfect CSI. IEEE Transactions on Wireless Communications, 13(5), 2809–2823.CrossRefGoogle Scholar
  15. 15.
    Hu, B., Hua, C., Zhang, J., et al. (2017). Joint fronthaul multicast beamforming and user-centric clustering in downlink C-RANs. IEEE Transactions on Wireless Communications, 16(8), 5395–5409.CrossRefGoogle Scholar
  16. 16.
    Teng, Y., & Zhao, W. (2017). Robust group sparse beamforming for dense C-RANs with probabilistic SINR constraints. In Wireless communications and networking conference (WCNC), 2017. IEEE, San Francisco, USA, March 2017.
  17. 17.
    Nigam, G., Minero, P., & Haenggi, M. (2014). Coordinated multipoint joint transmission in heterogeneous networks. IEEE Transactions on Communications, 62, 4134–4146.CrossRefGoogle Scholar
  18. 18.
    Wang, H. M., & Zheng, T. X. (2016). Physical layer security in random cellular networks. Berlin: Springer.CrossRefGoogle Scholar
  19. 19.
    Guo, A., Zhong, Y., Zhang, W., et al. (2016). The Gauss–Poisson process for wireless networks and the benefits of cooperation. IEEE Transactions on Communications, 64, 1916–1929.CrossRefGoogle Scholar
  20. 20.
    Tanbourgi, R., Singh, S., Andrews, J., et al. (2014). A tractable model for non-coherent joint-transmission base station cooperation. IEEE Transactions on Wireless Communications, 13, 4959–4973.CrossRefGoogle Scholar
  21. 21.
    Andrews, J. G., Baccelli, F., & Ganti, R. K. (2011). A tractable approach to coverage and rate in cellular networks. IEEE Transactions on Communications, 59, 3122–3134.CrossRefGoogle Scholar
  22. 22.
    Ding, Z., & Poor, H. V. (2013). The use of spatially random base stations in cloud radio access networks. IEEE Signal Processing Letters, 20(11), 1138–1141.CrossRefGoogle Scholar
  23. 23.
    Peng, M., Yan, Sh, & Poor, H. V. (2014). Ergodic capacity analysis of remote radio head associations in cloud radio access networks. IEEE Wireless Communications Letters, 3(4), 365–368.CrossRefGoogle Scholar
  24. 24.
    Wang, H. M., & Zheng, T. X. (2016). Physical layer security in random cellular networks. SpringerBriefs in Computer Science. Springer.Google Scholar
  25. 25.
    Newman, D. S. (1970). A new family of point processes which are characterized by their second moment properties. Journal of Applied Probability, 7, 338–358.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Haenggi, M. (2012). Stochastic geometry for wireless networks. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  27. 27.
    Bartelt, J., Rost, P., Wubben, D., et al. (2015). Fronthaul and backhaul requirements of flexibly centralized radio access networks. IEEE Wireless Communications, 22, 105–111.CrossRefGoogle Scholar
  28. 28.
    Hajisami, A., & Pompili, D. (2015). Cloud-CFFR: Coordinated fractional frequency reuse in cloud radio access network (C-RAN). In 2015 IEEE 12th international conference on mobile ad hoc and sensor systems (pp. 46–54).Google Scholar
  29. 29.
    Björnson, E., Sanguinetti, L., Hoydis, J., et al. (2015). Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer? IEEE Transactions on Wireless Communications, 14, 3059–3075.CrossRefGoogle Scholar
  30. 30.
    Sabella, D., De Domenico, A., Katranaras, E., et al. (2014). Energy efficiency benefits of RAN-as-a-service concept for a cloud-based 5G mobile network infrastructure. IEEE Access, 2, 1586–1597.CrossRefGoogle Scholar
  31. 31.
    Peng, M., Yu, Y., Xiang, H., & Poor, H. V. (2015). Energy-efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks. IEEE Transactions on Vehicular Technology, 64, 5275–5278.CrossRefGoogle Scholar
  32. 32.
    Qin, C., Ni, W., Tian, H., & Liu, R. P. (2017). Fronthaul load balancing in energy harvesting powered cloud radio access networks. IEEE Access, 5, 7762–7775.CrossRefGoogle Scholar
  33. 33.
    Amjad, M., Akhtar, F., Rehmani, M. H., et al. (2017). Full-duplex communication in cognitive radio networks: A survey. IEEE Communiications Surveys and Tutorials, 19(4), 2158–2191.CrossRefGoogle Scholar
  34. 34.
    Li, Y., Liu., J., Cao, B., & Wang, C. (2018). Joint optimization of radio and virtual machine resources with uncertain user demands in mobile cloud computing. IEEE Transactions on Multimedia. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran

Personalised recommendations