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Reliability Enhancement of URLLC Traffic in 5G Cellular Networks

  • Jerzy MartynaEmail author
Conference paper
  • 66 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1231)

Abstract

5G cellular networks must be able to deliver a small data payload in a very short time (up to 1 ms) with ultra-high probability of success (99.999%) to the mobile user. Achieving ultra-reliable and low-latency communication (URLLC) represents one of the major challenges in terms of system design. This paper covers definitions of latency and the reliability of URLLC traffic. Furthermore, it presents a method for reliability enhancement of URLLC traffic. To this end, the problem of reliability enhancement is formulated as an optimisation problem, the objective of which is to maximise the sum of data rates for all users with the URLLC constraints. Simulation results show that the suggested method validates the proposed model.

Keywords

5G systems Wireless scheduling URLLC traffic eMBB traffic Reliability 

References

  1. 1.
    ITU-R M.2083-0, IMT Vision - Framework and overall objectives of the future development of IMT for 2020 and beyond, September 2015Google Scholar
  2. 2.
    3GPP TSG RAN WG1 Meeting 87, November 2016Google Scholar
  3. 3.
    3GPP TR 38.913 V14.2.0, 5G; Study on Scenarios and Requirements for Next Generation Access Technologies (2017)Google Scholar
  4. 4.
    Zhang, L., Ijaz, A., Xiao, P., Quddus, A., Tafazolli, R.: Subband filtered multi-carrier systems for multi-service wireless communications. IEEE Trans. Wireless Comm. 16(3), 1893–1907 (2017)CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Ijaz, A., Xiao, P.: Multi-service system: an enabler of flexible 5G air-interface. IEEE Commun. Mag. 55(10), 152–159 (2017)CrossRefGoogle Scholar
  6. 6.
    Pedersen, K., Pocovi, G., Steiner, J., Maeder, A.: Agile 5G scheduler for improved E2E performance and flexibility for different network implementations. IEEE Commun. Mag. 56(3), 210–217 (2018)CrossRefGoogle Scholar
  7. 7.
    Kowalski J. M., Nogami T., Yin Z., Sheng J., Ying K.: Coexistence of enhanced mobile broadband communications and ultra reliable low latency communications in mobile fronthaul. In: Broadband Access Communication Technologies XII, no. January, p. 11 (2018)Google Scholar
  8. 8.
    Anand, A., Veciana, G., Shakkottai, S.: Joint scheduling of URLLC and eMBB traffic in 5G wireless networks. IEEE International Conference on Computing Communication, Honolulu, USA (2018)Google Scholar
  9. 9.
    Esswie, A.A., Pedersen, K.I.: Opportunistic spatial preemptive scheduling for URLLC and eMBB coexistence in multi-user 5G networks. IEEE Access 6, 38451–38463 (2018)CrossRefGoogle Scholar
  10. 10.
    Hoymann, C., et al.: LTE release 14 outlook. IEEE Commun. Mag. 54(6), 44–49 (2016)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Schulz, P., et al.: Latency critical IoT application in 5G: perspective on the design of radio access networks. IEEE Trans. Wirel. Commun. 55(2), 70–78 (2017)Google Scholar
  13. 13.
    She, C., Yang, C., Quek, T.S.: Cross-layer optimization for ultra-reliable and low-latency radio access networks. IEEE Trans. Wirel. Commun. 17(1), 127–141 (2018)CrossRefGoogle Scholar
  14. 14.
    Anwar, W., Kulkarni, K., Franchi, N., Fettweis, G.: Physical layer abstraction for ultra-reliable communications in 5G multi-connectivity networks. In: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Italy, Bologna (2018)Google Scholar
  15. 15.
    Rao, J., Vrzic, S.: Packet duplication for URLLC in 5G dual connectivity architecture. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, April 2018Google Scholar
  16. 16.
    Harchol-Balter, M.: Performance Modeling and Design of Computer System: Queueing Theory in Action. Cambridge University Press, Cambridge (2013)zbMATHGoogle Scholar
  17. 17.
    Anand, A., de Veciana, G.: Resource allocation and HARQ optimization for URLLC traffic in 5G wireless networks. http://arxiv.org/abs/1804.09201
  18. 18.
    Jang, J., Lee, K.B.: Transmit power adaptation for multiuser OFDM systems. IEEE J. Sel. Areas Commun. 21(2), 171–178 (2003)CrossRefGoogle Scholar
  19. 19.
    Burge, J.R., Louveaux, F.V.: Introduction to Stochastic Programming. Springer, New York (1997).  https://doi.org/10.1007/b97617CrossRefGoogle Scholar
  20. 20.
    Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows. SIAM J. Comput. 31(6), 1794–1813 (2002)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Kushner, H.J., Whiting, P.A.: Convergence of proportional-fair sharing algorithms under general conditions. IEEE Trans. Wirel. Commun. 3(4), 1250–1259 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Computer Science, Faculty of Mathematics and Computer ScienceJagiellonian UniversityCracowPoland

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