Advertisement

Quality-focused resource allocation for resilient 5G network

  • Rasa BruzgieneEmail author
  • Lina Narbutaite
  • Tomas Adomkus
Original Paper
  • 37 Downloads

Abstract

The upcoming 5G cellular wireless network brings new challenges and problematic issues in providing services with different quality of service (QoS) requirements and serving huge amount of mobile devices in a spectrum-efficient manner. 5G-based systems will combine macrocells, different type of small cells and heterogeneous networks. As a result of this combination, a 5G-based network will feature a sophisticated multi-layered architecture, and a proper resource allocation will become a major challenge for it. A reliable provision of services as well. In this paper, the analysis of the impact of different QoS schedule algorithms (Round Robin, Best CQI and PF) to the allocation of resources and a reliability of data transmission in 5G network was carried out. Also, the relation of QoS characteristics (BER, data loss) to the perceptual evaluation of service quality by the end user in different ways of the resource allocation on 5G network was investigated also. The perceptual evaluation of a service quality, known as Quality of Experience, was investigated using mean opinion score method.

Keywords

5G QoE QoS Resource allocation Scheduling 

Notes

Acknowledgements

This research is based upon work from COST Action CA15127 (Resilient communication services protecting end-user applications from disaster-based failures—RECODIS) supported by COST (European Cooperation in Science and Technology).

References

  1. 1.
    VanGiang, N., Anna, B., KarlJohan, G., Javid, T.: 5G Mobile Networks, ch. 2, pp. 31–57. Wiley, New York (2018)Google Scholar
  2. 2.
    Marsch, P., Da Silva, I., Bulakci, O., Tesanovic, M., El Ayoubi, S.E., Rosowski, T., Kaloxylos, A., Boldi, M.: 5G radio access network architecture: design guidelines and key considerations. IEEE Commun. Mag. 54(11), 24–32 (2016)CrossRefGoogle Scholar
  3. 3.
    Zou, J., Wagner, C., Eiselt, M.: Optical fronthauling for 5G mobile: a perspective of passive metro WDM technology. In: Optical Fiber Communication Conference. Optical Society of America, p. W4C.2 (2017)Google Scholar
  4. 4.
    Infrastructure Association, et al.: 5G vision—the 5G infrastructure public private partnership; the next generation of communication networks and services, White Paper. Brussels (2015, February)Google Scholar
  5. 5.
    Chen, K., Duan, R.: C-RAN the road towards green RAN, White Paper, vol. 2. China Mobile Research Institute (2011)Google Scholar
  6. 6.
    Lin, Y., Shao, L., Zhu, Z., Wang, Q., Sabhikhi, R .K.: Wireless network cloud: architecture and system requirements. IBM J. Res. Dev. 54, 4:1–4:12 (2010)CrossRefGoogle Scholar
  7. 7.
    Checko, A., Avramova, A.P., Berger, M.S., Christiansen, H.L.: Evaluating c-ran fronthaul functional splits in terms of network level energy and cost savings. J. Commun. Netw. 18, 162–172 (2016)CrossRefGoogle Scholar
  8. 8.
    Huawei: 5G Network Architecture: A High-Level Perspective. White Paper. Huawei Technologies Co, Shenzhen (2016)Google Scholar
  9. 9.
    Murphy, K.: Centralized ran and fronthaul, White Paper. Ericsson, Stockholm (2015)Google Scholar
  10. 10.
    Skubic, B., Fiorani, M., Tombaz, S., Furuskär, A., Mårtensson, J., Monti, P.: Optical transport solutions for 5G fixed wireless access. J. Opt. Commun. Netw. 9, D10–D18 (2017)CrossRefGoogle Scholar
  11. 11.
    Honda, K., Nakamura, H., Hara, K., Sone, K., Nakagawa, G., Hirose, Y., Hoshida, T., Terada, J., Otaka, A.: Wavelength adjustment of upstream signal using amcc with power monitoring for WDM-PON in 5G mobile era. In: Optical Fiber Communication Conference. Optical Society of America, p. Tu3L.4 (2018)Google Scholar
  12. 12.
    Suzuki, N., Miura, H., Matsuda, K., Matsumoto, R., Motoshima, K.: 100 gb/s to 1 tb/s based coherent passive optical network technology. J. Lightwave Technol. 36, 1485–1491 (2018)CrossRefGoogle Scholar
  13. 13.
    Jia, Z., Yu, J., Chien, H.C., Dong, Z., Huo, D.D.: Field transmission of 100 g and beyond: multiple baud rates and mixed line rates using Nyquist-WDM technology. J. Lightwave Technol. 30, 3793–3804 (2012)CrossRefGoogle Scholar
  14. 14.
    Bao, X., Wang, G., Hou, Z., Xu, M., Peng, L., Han, H.: WDM switch technology application in smart substation communication network. In: 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp. 2373–2376 (2015)Google Scholar
  15. 15.
    Hossain, E., Hasan, M.: 5G cellular: Key enabling technologies and research challenges. CoRR (2015). arXiv1503.00674Google Scholar
  16. 16.
    Hajjawi, A., Ismail, M., Abdullah, N. F., Hindia, M. N., Al-Samman, A. M., Hanafi, E.: Investigation of the impact of different scheduling algorithm for macro-femto-cells over lte-a networks. In: 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT). IEEE, pp. 125–128 (2016)Google Scholar
  17. 17.
    Ghariani, T., Jouaber, B.: Energy consumption evaluation for lte scheduling algorithms. In: 2015 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, pp. 1–5 (2015)Google Scholar
  18. 18.
    Ghasemzadeh, M.: Qos based resource management for cloud environment. Master thesis (2016)Google Scholar
  19. 19.
    Nikaein, N., Schiller, E., Favraud, R., Knopp, R., Alyafawi, I., Braun, T.: Towards a cloud-native radio access network. Advances in Mobile Cloud Computing and Big Data in the 5G Era, pp. 171–202. Springer, Berlin (2017)CrossRefGoogle Scholar
  20. 20.
    Chang, C. Y., Schiavi, R., Nikaein, N., Spyropoulos, T., Bonnet, C.: Impact of packetization and functional split on c-ran fronthaul performance. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–7 (2016)Google Scholar
  21. 21.
    Khalili, S., Simeone, O.: Uplink harq for cloud ran via separation of control and data planes. IEEE Trans. Veh. Technol. 66, 4005–4016 (2017)CrossRefGoogle Scholar
  22. 22.
    Peng, M., Sun, Y., Li, X., Mao, Z., Wang, C.: Recent advances in cloud radio access networks: system architectures, key techniques, and open issues. IEEE Commun. Surv. Tutor. 18, 2282–2308 (2016)CrossRefGoogle Scholar
  23. 23.
    Tran, T.X., Hajisami, A., Pompili, D.: Cooperative hierarchical caching in 5G cloud radio access networks. IEEE Netw. 31(4), 35–41 (2017)CrossRefGoogle Scholar
  24. 24.
    Fakhri, Z. H., Khan, M., Sabir, F., Al-Raweshidy, H. S.: A resource allocation mechanism for cloud radio access network based on cell differentiation and integration concept. IEEE Trans. Netw. Sci. Eng. 5(4), 261–275 (2017)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electronics EngineeringKaunas University of TechnologyKaunasLithuania
  2. 2.Department of Software EngineeringKaunas University of TechnologyKaunasLithuania
  3. 3.Department of Computer SciencesKaunas University of TechnologyKaunasLithuania

Personalised recommendations