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Resource-efficiency improvement based on BBU/RRH associated scheduling for C-RAN

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Abstract

Cloud radio access network (C-RAN) is an attractive technology to improve power efficiency through providing a novel architecture of baseband unit (BBU) pool to centrally process mass of transmitted data. How to further reduce BBU pool resource cost is a challenge issue. In this paper, we propose a resource scheduling scheme jointly considering multi-cell interference and BBU pool resource cost to reduce system power consumption. We construct the total power consumption, which includes computing resource of BBU pool and data transmission power. And we formulate the resource scheduling cost (RSC) model to minimize the total power consumption per bit. To solve the proposed RSC problem, which is NP-hard, we equivalently transform the original RSC problem into two sub-problems, i.e. transmission power and computing resource sub-problems. We apply support vector machine to compute the probabilistic interference and obtain the optimized remote radio heads transmission power and transmit rate using Zoutendijk’s method. In addition, we solve the computing resource sub-problem applying linear programming to obtain the minimum computing resource cost of BBU pool and system power cost. Simulation results show that the proposed RSC model significantly improved resource efficiency compared with the decomposition model for BBU allocation and NF based BBU scheduling model.

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References

  1. 1.

    Wang, C.-X., et al. (2014). Cellular architecture and key technologies for 5G wireless communication networks. IEEE Communications Magazine, 52(2), 122–130.

  2. 2.

    Xu, J., et al. (2018). Resource allocation for real-time traffic in unreliable wireless cellular networks. Wireless Networks, 24, 1405–1418.

  3. 3.

    Alimi, I. A., Teixeira, A. L., & Monteiro, P. P. (2018). Toward an efficient C-RAN optical fronthaul for the future networks: A tutorial on technologies, requirements, challenges, and solutions. IEEE Communications Surveys & Tutorials, 20(1), 708–769.

  4. 4.

    Checko, A., et al. (2015). Cloud RAN for mobile networks—A technology overview. IEEE Communications Surveys & Tutorials, 17(1), 405–426.

  5. 5.

    Rodriguez, V. Q., & Guillemin, F. (2018). Cloud-RAN modeling based on parallel processing. IEEE Journal on Selected Areas in Communications, 36(3), 457–468.

  6. 6.

    Garcia-Saavedra, A., et al. (2018). WizHaul: On the centralization degree of cloud RAN next generation fronthaul. IEEE Transactions on Mobile Computing, 17, 2452–2466.

  7. 7.

    Dabbagh, M., et al. (2014). Energy-efficient cloud resource management. In INFOCOM workshops.

  8. 8.

    Liao, Y., et al. (2017). How much computing capability is enough to run a cloud radio access network? IEEE Communications Letters, 21(1), 104–107.

  9. 9.

    Yang, Z., Ding, Z., & Fan, P. (2016). Performance analysis of cloud radio access networks with uniformly distributed base stations. IEEE Transactions on Vehicular Technology, 65(1), 472–477.

  10. 10.

    Tang, J., et al. (2017). System cost minimization in cloud RAN with limited fronthaul capacity. IEEE Transactions on Wireless Communications, 16(5), 3371–3384.

  11. 11.

    Zhan, S.-C., & Niyato, D. (2017). A coalition formation game for remote radio head cooperation in cloud radio access network. IEEE Transactions on Vehicular Technology, 66(2), 1723–1738.

  12. 12.

    Park, S.-H., et al. (2016). Joint design of fronthaul and access links for C-RAN with wireless fronthauling. IEEE Signal Processing Letters, 23(11), 1657–1661.

  13. 13.

    Chen, Y.-S., Chiang, W.-L., & Shih, M.-C. (2018). A dynamic BBU–RRH mapping scheme using borrow-and-lend approach in cloud radio access networks. IEEE Systems Journal, 12(2), 1632–1643.

  14. 14.

    Qian, M., et al. (2015). Baseband processing units virtualization for cloud radio access networks. IEEE Wireless Communications Letters, 4(2), 189–192.

  15. 15.

    Xu, S., & Wang, S. (2017). Baseband unit pool planning for cloud radio access networks: An approximation algorithm. IEEE Communications Letters, 21(1), 358–361.

  16. 16.

    Ye, Z., et al. (2018). Tradeoff caching strategy of outage probability and fronthaul usage in Cloud-RAN. IEEE Transactions on Vehicular Technology, 67, 6383–6397.

  17. 17.

    Zhu, D., & Lei, M. (2013). Traffic and interference-aware dynamic BBU-RRU mapping in C-RAN TDD with cross-subframe coordinated scheduling/beamforming. In 2013 IEEE International conference on communications workshops (ICC). IEEE.

  18. 18.

    Shi, Y., Zhang, J., & Letaief, K. B. (2014). Group sparse beamforming for green cloud-RAN. IEEE Transactions on Wireless Communications, 13(5), 2809–2823.

  19. 19.

    Dai, B., & Yu, W. (2014). Sparse beamforming and user-centric clustering for downlink cloud radio access network. IEEE Access, 2, 1326–1339.

  20. 20.

    Huang, X., et al. (2016). Joint scheduling and beamforming coordination in cloud radio access networks with QoS guarantees. IEEE Transactions on Vehicular Technology, 65(7), 5449–5460.

  21. 21.

    36819-b20, G. (2013). Coordinated multi-point operation for LTE physical layer aspects, 3GPP.

  22. 22.

    Auer, G., et al. (2011). How much energy is needed to run a wireless network? IEEE Wireless Communications, 18(5), 40–49.

  23. 23.

    Gestel, T. V., et al. (2002). Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis. Neural Computation, 14(5), 1115–1147.

  24. 24.

    Aqeeli, E., Moubayed, A., & Shami, A. (2018). Power-aware optimized RRH to BBU allocation in C-RAN. IEEE Transactions on Wireless Communications, 17(2), 1311–1322.

  25. 25.

    Sigwele, T., et al. (2015). Evaluating energy-efficient cloud radio access networks for 5G. In 2015 IEEE international conference on data science and data intensive systems. IEEE.

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Acknowledgements

This work was supported by National Science and Technology Major Project No. 2017ZX03001021-005.

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Correspondence to Yueyun Chen.

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Yang, L., Chen, Y. Resource-efficiency improvement based on BBU/RRH associated scheduling for C-RAN. Wireless Netw 25, 2805–2815 (2019). https://doi.org/10.1007/s11276-019-01995-8

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Keywords

  • Cloud radio access network (C-RAN)
  • Baseband unit (BBU) pool
  • Computing resource
  • Power cost