Skip to main content

Performance Evaluation of URLLC in 5G Based on Stochastic Network Calculus

Abstract

Ultra-reliable low latency communications (URLLC) is one of the most important scenarios in 5G. URLLC with strict latency and reliability requirements is widely used in delay-sensitive application such as self-driving. As the 3GPP claims, the URLLC is amenable to 99.999% transmission correctness and within 1ms delay bound. How to meet the requirements of reliability and latency is still an open issue. Few efforts have been made to applying a theoretical method to analyze the delay bound. Stochastic network calculus is an elegant way to obtain the delay bound based on traffic models and service guarantees. In this paper, we take the character of 5G architecture into account and use the stochastic network calculus to analyze the delay in URLLC. A tandem model describing the communication in the 5G network is built, and parameters which have an influence on the delay are analyzed. Numerical results are presented to verify the correctness of the delay analysis. We investigate how to optimize the parameters to reduce the delay, which would provide valuable guidelines for the design of URLLC architecture.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    ITU-R M.2083-0 (2015) IMT vision - framework and overall objectives of the future development of IMT for 2020 and beyond

  2. 2.

    3GPP TR 38.913 (2017) Study on scenarios and requirements for next generation access technologies

  3. 3.

    Soldani D, Guo YJ, Barani B, et al. (2018) 5G for ultra-reliable low-latency communications. IEEE Netw 32(2):6–7

    Article  Google Scholar 

  4. 4.

    Jiang Y, Liu Y (2009) Stochastic network calculus. Springer, London

    MATH  Google Scholar 

  5. 5.

    Fidler M, Rizk A (2015) A guide to the stochastic network calculus. IEEE Commun Surv Tutor 17(1):92–105

    Article  Google Scholar 

  6. 6.

    Nielsen JJ, Liu R, Popovski P (2018) Ultra-reliable low latency communication using interface diversity. IEEE Trans Commun 66(3):1322–1334

    Article  Google Scholar 

  7. 7.

    Delgado RA, Lau K, Middleton RH, et al. (2018) Networked delay control for 5G wireless machine-type communications using multiconnectivity. IEEE Trans Control Syst Technol 99:1–16

    Google Scholar 

  8. 8.

    Rao J, Vrzic S (2018) Packet duplication for URLLC in 5G: architectural enhancements and performance analysis. IEEE Netw 32(2):32–40

    Article  Google Scholar 

  9. 9.

    Anand A, De Veciana G (2018) Resource allocation and HARQ optimization for URLLC traffic in 5G wireless networks

  10. 10.

    Mukherjee A (2018) Energy efficiency and delay in 5G ultra-reliable low-latency communications system architectures. IEEE Netw 32(2):55–61

    Article  Google Scholar 

  11. 11.

    Sachs J, Wikstrom G, Dudda T, et al. (2018) 5G radio network design for ultra-reliable low-latency communication. IEEE Netw 32(2):24–31

    Article  Google Scholar 

  12. 12.

    Wang C, Chen Y, Wu Y, et al. (2017) Performance evaluation of grant-free transmission for uplink URLLC services. IEEE Vehicular technology conference 2017 Vtc2017-Spring, pp 1–6

  13. 13.

    Pocovi G, Shariatmadari H, Berardinelli G, et al. (2018) Achieving ultra-reliable low-latency communications: challenges and envisioned system enhancements. IEEE Netw 32(2):8– 15

    Article  Google Scholar 

  14. 14.

    Popovski P, Nielsen JJ, Stefanovic C, et al. (2018) Wireless access for ultra-reliable low-latency communication: principles and building blocks. IEEE Netw 32(2):16–23

    Article  Google Scholar 

  15. 15.

    Ji H, Park S, Yeo J (2017) Introduction to ultra reliable and low latency communications in 5G

  16. 16.

    Ji H, Park S, Yeo J, et al. (2018) Ultra reliable and low latency communications in 5G downlink: physical layer aspects

  17. 17.

    Li Z, Jiang Y, Gao Y, Li P, Sang L, Yang D (2017) Delay and delay-constrained throughput performance of a wireless-powered communication system. IEEE Access 5:21620–21631

    Article  Google Scholar 

  18. 18.

    Lubben R, Fidler M (2016) Estimation method for the delay performance of closed-loop flow control with application to TCP. In: IEEE INFOCOM 2016-The 35th Annual IEEE international conference on computer communications, pp 1–9

  19. 19.

    Zheng K, Liu F, Lei L, Lin C, Jiang Y (2013) Stochastic performance analysis of a wireless finite-state Markov channel. IEEE Trans Wirel Commun 12(2):782–793

    Article  Google Scholar 

  20. 20.

    Chen Xin, Si Y, Xiang X (2015) Delay-bounded resource allocation for femtocells exploiting the statistical multiplexing gain, 71:3217–3236

  21. 21.

    Fidler M, Walker B, Jiang Y (2018) Non-asymptotic delay bounds for multi-server systems with synchronization constraints. IEEE Trans Parallel Distrib Syst 29(7):1545–1559

    Article  Google Scholar 

  22. 22.

    Guang Y, Xiao M, Poor HV (2018) Low-latency millimeter-wave communications: traffic dispersion or network densification? IEEE Trans Commun 66(8):3526–3539

    Article  Google Scholar 

  23. 23.

    Lubben R, Fidler M, Liebeherr J (2014) Stochastic bandwidth estimation in networks with random service. IEEE/ACM Trans Network 22(2):484–497

    Article  Google Scholar 

  24. 24.

    Gulyas A, Biro J (2006) A stochastic extension of network calculus for workload loss examinations. Commun Lett IEEE 10(5):399–401

    Article  Google Scholar 

  25. 25.

    Deng Y, Lin C (2010) An extended stochastic loss bound with moment generating function. In: International conference on communications & mobile computing IEEE

  26. 26.

    Ayyorgun S, et al. (2004) A composable service model with loss and a scheduling algorithm. Infocom

  27. 27.

    Wu K, Jiang Y, Li J (2010) On the model transform in stochastic network calculus. In: International workshop on quality of service. IEEE, pp 1–9

  28. 28.

    Sun F, Li L, Jiang Y (2015) Impact of duty cycle on end-to-end performance in a wireless sensor network. In: Wireless communications and networking conference. IEEE, pp 1906–1911

  29. 29.

    Beck M (2016) Towards the analysis of transient phases with stochastic network calculus. In: Telecommunications network strategy and planning symposium. IEEE, pp 164–169

  30. 30.

    Chen Y, Zhang N, Zhang Y, Chen X, Wu W, Shen X (2019) Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things. In: IEEE Transactions on Cloud Computing

Download references

Acknowledgments

Supported by program National Natural Science Foundation of China (Nos.61872044,61502040). Beijing Municipal Program for Excellent Teacher Promotion (No.PXM2017_014224. 000028). Beijing Municipal Program for Top Talent Cultivation (CIT&TCD201804055). Qinxin Talent Program of Beijing Information Science and Technology University.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shengcheng Ma.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, S., Chen, X., Li, Z. et al. Performance Evaluation of URLLC in 5G Based on Stochastic Network Calculus. Mobile Netw Appl 26, 1182–1194 (2021). https://doi.org/10.1007/s11036-019-01344-1

Download citation

Keywords

  • 5G
  • URLLC
  • Stochastic network calculus
  • Delay analysis