Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Extreme ultra-reliable and low-latency communication

Abstract

Ultra-reliable and low-latency communication (URLLC) is central to fifth-generation (5G) communication systems, but the fundamentals of URLLC remain elusive. New immersive and high-stake control applications with stricter reliability, latency and scalability requirements are now also creating unprecedented challenges for URLLC. Here we examine the limitations of 5G URLLC and propose key research directions for the next generation of URLLC, which we term extreme ultra-reliable and low-latency communication (xURLLC). xURLLC is underpinned by three concepts: the leveraging of recent advances in machine learning for faster and more reliable data-driven predictions; complementing radiofrequency signal transmission with non-radiofrequency data and passive signal reflection to combat rare events at scale; emphasizing joint communication and control co-design, as opposed to the communication-centric approach of 5G URLLC. For each of these concepts, we consider the challenges and opportunities, and illustrate the effectiveness of the proposed solutions through selected use cases.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Anatomy of xURLLC with key research challenges and opportunities, R1–R9.
Fig. 2: Predictive URLLC use cases.
Fig. 3: Non-transmissive URLLC use cases.
Fig. 4: Control co-designed URLLC use cases.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The simulation in the section ‘ML-based energy-efficient RIS’ was implemented using TensorFlow and the rest of the simulations were based on MATLAB. The simulation settings in the section ‘Predictive AoI for ultra-reliable V2V communication’ follow from ref. 31. The section ‘VR/augmented reality perception-aware proactive network slicing’ is based on refs. 32,40. The section ‘ML-based energy-efficient RIS’ is based on ref. 65 and the SimRIS channel simulator66. The section ‘RGB-D aided mmWave received power prediction’ is based on refs. 43,44. Finally, the sections ‘ML-aided single UAV remote control’ and ‘ML-aided massive autonomous UAV control’ are based on ref. 51 and ref. 52, respectively. The detailed simulation codes of this study are available from the corresponding authors upon reasonable request.

References

  1. Le, T.-K., Salim, U. & Kaltenberger, F. An overview of physical layer design for ultra-reliable low-latency communications in 3GPP releases 15, 16, and 17. IEEE Access 9, 433–444 (2021).

    Article  Google Scholar 

  2. Bennis, M., Debbah, M. & Poor, V. Ultra-reliable and low-latency wireless communication: tail, risk and scale. Proc. IEEE 106, 1834–1853 (2018).

    Article  Google Scholar 

  3. Swamy, V. N. et al. Monitoring under-modeled rare events for URLLC. In Proc. 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (eds Zochmann, E. et al.) 1–5 (IEEE, 2019).

  4. Popovski, P. et al. Wireless access for ultra-reliable low-latency communication (URLLC): principles and building blocks. IEEE Network 32, 16–23 (2018).

    Article  Google Scholar 

  5. Mahmood, A. et al. Time synchronization in 5G wireless edge: requirements and solutions for critical-MTC. IEEE Commun. Mag. 57, 45–51 (2019).

    Article  Google Scholar 

  6. Ji, H. et al. Ultra-reliable and low-latency communications in 5G downlink: physical layer aspects. IEEE Wirel. Commun. 25, 124–130 (2018).

    Article  Google Scholar 

  7. Study on Physical Layer Enhancements for NR Ultra-reliable and Low Latency Case (URLLC) Technical Report 38.824 Rel-16 (3GPP, 2019).

  8. Centenaro, M., Laselva, D., Steiner, J., Pedersen, K. & Mogensen, P. Resource-efficient dual connectivity for ultra-reliable low-latency communication. In Proc. IEEE 91st Vehicular Technology Conference (VTC2020-Spring) 1–5 (IEEE, 2020).

  9. Liu, Y., Deng, Y., Elkashlan, M., Nallanathan, A. & Karagiannidis, G. K. Analyzing grant-free access for URLLC service. IEEE J. Sel. Areas Commun. 39, 741–755 (2021).

    Article  Google Scholar 

  10. de Amorim, R. M., Wigard, J., Kovacs, I., Sorensen, T. B. & Mogensen, P. E. Enabling cellular communication for aerial vehicles: providing reliability for future applications. IEEE Veh. Technol. Mag. 15, 129–135 (2020).

    Article  Google Scholar 

  11. Berardinelli, G., Mahmood, N. H., Rodriguez, I. & Mogensen, P. Beyond 5G wireless IRT for industry 4.0: design principles and spectrum aspects. In Proc. 2018 IEEE Globecom Workshops 1–6 (IEEE, 2018).

  12. Time-sensitive Networking: A Technical Introduction White Paper (Cisco, 2017); https://www.cisco.com/c/dam/en/us/solutions/collateral/industry-solutions/white-paper-c11-738950.pdf

  13. 6Genesis Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence White Paper, Vol. 1 (Univ. Oulu, 2019).

  14. Dang, S., Amin, O., Shihada, B. & Alouini, M.-S. What should 6G be? Nat. Electron. 3, 20–29 (2002).

    Article  Google Scholar 

  15. Saad, W., Bennis, M. & Chen, M. A vision of 6G wireless systems: applications, trends, technologies and open research problems. IEEE Netw. 34, 134–142 (2020).

    Article  Google Scholar 

  16. Viswanathan, H. & Mogensen, P. E. Communications in the 6G era. IEEE Access 8, 57063–57074 (2020).

    Article  Google Scholar 

  17. Park, J., Samarakoon, S., Bennis, M. & Debbah, M. Wireless network intelligence at the edge. Proc. IEEE 107, 2204–2239 (2019).

    Article  Google Scholar 

  18. Pokhrel, S. R., Ding, J., Park, J., Park, O.-S. & Choi, J. Towards enabling critical mMTC: a review of URLLC within mMTC. IEEE Access 8, 131796–131813 (2020).

    Article  Google Scholar 

  19. Angjelichinoski, M., Trillingsgaard, K. F. & Popovski, P. A statistical learning approach to ultra-reliable low latency communication. IEEE Trans. Commun. 67, 5153–5166 (2019).

    Article  Google Scholar 

  20. Kasgari, A. T. Z. & Saad, W. Model-free ultra reliable low latency communication (URLLC): a deep reinforcement learning framework. In Proc. IEEE International Conference on Communications (ICC) 1–6 (IEEE, 2019).

  21. Khan, H., Butt, M. M., Samarakoon, S., Sehier, P. & Bennis, M. Deep learning assisted CSI estimation for joint URLLC and eMBB resource allocation. In Proc. 2020 IEEE International Conference on Communications Workshops 1–6 (IEEE, 2020).

  22. Koda, Y. et al. One pixel image and RF signal based split learning for mmWave received power prediction. In Proc. 15th International Conference on Emerging Networking Experiments and Technologies 54–56 (ACM, 2019); https://doi.org/10.1145/3360468.3368176

  23. Nishio, T. et al. Proactive received power prediction using machine learning and depth images for mmWave networks. IEEE J. Select. Areas Commun. 37, 2413–2427 (2019).

    Article  Google Scholar 

  24. Tariq, F. et al. A speculative study on 6G. IEEE Wirel. Commun. 27, 118–125 (2020).

    Article  Google Scholar 

  25. Matthaiou, M. et al. The road to 6G: ten physical layer challenges for communications engineers. IEEE Commun. Mag. 59, 64–69 (2021).

    Article  Google Scholar 

  26. Hoydis, J., Aoudia, F. A., Valcarce, A. & Viswanathan, H. Towards a 6G AI-native air interface. IEEE Commun. Mag. 59, 76–81 (2021).

    Article  Google Scholar 

  27. Dizdar, O., Mao, Y., Han, W. & Clerckx, B. Rate-splitting multiple access: a new frontier for the PHY layer of 6G. In Proc. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) 1–7 (IEEE, 2020).

  28. Giordani, M. & Zorzi, M. Non-terrestrial networks in the 6G era: challenges and opportunities. IEEE Netw. 35, 244–251 (2020).

    Article  Google Scholar 

  29. Rappaport, T. S. et al. Wireless communications and applications above 100 GHz: opportunities and challenges for 6G and beyond. IEEE Access 7, 78729–78757 (2019).

    Article  Google Scholar 

  30. She, C. et al. A tutorial on ultrareliable and low-latency communications in 6G: integrating domain knowledge into deep learning. Proc. IEEE 109, 204–246 (2021).

    Article  Google Scholar 

  31. Abdel-Aziz, M. K., Samarakoon, S., Bennis, M. & Saad, W. Ultra-reliable and low-latency vehicular communication: an active learning approach. IEEE Commun. Lett. 24, 367–370 (2020).

    Article  Google Scholar 

  32. Park, J. & Bennis, M. URLLC-eMBB slicing to support VR multimodal perceptions over wireless cellular systems. In Proc. 2018 IEEE Global Communications Conference (GLOBECOM) 1–7 (IEEE, 2018).

  33. Samarakoon, S., Bennis, M., Saad, W. & Debbah, M. Distributed federated learning for ultra-reliable low-latency vehicular communications. IEEE Trans. Commun. 68, 1146–1159 (2019).

    Article  Google Scholar 

  34. Coles, S., Bawa, J., Trenner, L. & Dorazio, P. An Introduction to Statistical Modeling of Extreme Values Vol. 208 (Springer, 2001).

  35. Holton, G. A. Value-at-Risk: Theory and Practice (Academic, 2003).

  36. Billard, A. & Grollman, D. in Encyclopedia of the Sciences of Learning (ed. Seel, N. M.) 1474–1476 (Springer, 2012).

  37. Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  Google Scholar 

  38. Abdulla, M. & Wymeersch, H. Fine-grained vs. average reliability for V2V communications around intersections. In Proc. 2017 IEEE Globecom Workshops (GC Wkshps) 1–5 (IEEE, 2017).

  39. Jaynes, E. T. Information theory and statistical mechanics. Phys. Rev. 106, 620–630 (1957).

    MathSciNet  Article  Google Scholar 

  40. Perfecto, C., Elbamby, M. S., Ser, J. D. & Bennis, M. Taming the latency in multi-user VR 360°: a QoE-aware deep learning-aided multicast framework. IEEE Trans. Commun. 68, 2491–2508 (2020).

    Article  Google Scholar 

  41. Koda, Y. et al. Communication-efficient multimodal split learning for mmWave received power prediction. IEEE Commun. Lett. 24, 1284–1288 (2020).

    Article  Google Scholar 

  42. Alahi, A., Haque, A. & Fei-Fei, L. RGB-W: when vision meets wireless. In Proc. 2015 IEEE International Conference on Computer Vision (ICCV) 3289–3297 (IEEE, 2015).

  43. Koda, Y. et al. Distributed heteromodal split learning for vision aided mmwave received power prediction. Preprint at https://arxiv.org/abs/2007.08208 (2020).

  44. Nishio, T., Koda, Y., Park, J., Bennis, M. & Doppler, K. When wireless communications meet computer vision in beyond 5G. IEEE Commun. Stand. Mag. 5, 76–83 (2021).

    Article  Google Scholar 

  45. Hu, S., Rusek, F. & Edfors, O. Beyond massive MIMO: the potential of data transmission with large intelligent surfaces. IEEE Trans. Signal Process. 66, 2746–2758 (2018).

    MathSciNet  Article  Google Scholar 

  46. Huang, C., Alexandropoulos, G. C., Zappone, A., Debbah, M. & Yuen, C. Energy efficient multi-user MISO communication using low resolution large intelligent surfaces. In Proc. IEEE Globecom Workshops (GC Workshops) 1–6 (IEEE, 2018).

  47. Nazemi, M., Pasandi, G. & Pedram, M. Energy-efficient, low-latency realization of neural networks through Boolean logic minimization. In Proc. 2019 ACM Asia and South Pacific Design Automation Conference 274–279 (ACM, 2019).

  48. Lawler, E. L. & Wood, D. E. Branch-and-bound methods: a survey. Oper. Res. 14, 699–719 (1966).

    MathSciNet  Article  Google Scholar 

  49. Nemati, M., Park, J. & Choi, J. RIS-assisted coverage enhancement in millimeter-wave cellular networks. IEEE Access 8, 188171–188185 (2020).

    Article  Google Scholar 

  50. Eisen, M. et al. Control aware radio resource allocation in low latency wireless control systems. IEEE Internet Things J. 6, 7878–7890 (2019).

    Article  Google Scholar 

  51. Shiri, H., Park, J. & Bennis, M. Remote UAV online path planning via neural network based opportunistic control. IEEE Wirel. Commun. Lett. 9, 861–865 (2020).

    Article  Google Scholar 

  52. Shiri, H., Park, J. & Bennis, M. Massive autonomous UAV path planning: a neural network based mean-field game theoretic approach. In Proc. 2019 IEEE Global Communications Conference (GLOBECOM) 1–6 (IEEE, 2019).

  53. Perdikaris, G. Computer Controlled Systems: Theory and Applications (Intelligent Systems, Control and Automation: Science and Engineering) (Springer, 1991); https://books.google.fi/books?id=Gzxh0TYX3TEC

  54. Sastry, S. Nonlinear Systems: Analysis, Stability and Control (Interdisciplinary Applied Mathematics) (Springer, 2013); https://books.google.fi/books?id=j_PiBwAAQBAJ

  55. Gazi, V. & Passino, K. M. Stability analysis of swarms. IEEE Trans. Automat. Contr. 48, 692–697 (2003).

    MathSciNet  Article  Google Scholar 

  56. Cook, P. Conditions for string stability. Syst. Contr. Lett. 54, 991–998 (2005).

    MathSciNet  Article  Google Scholar 

  57. Gueant, O., Lasry, J.-M. & Lions, P.-L. Paris-Princeton Lectures on Mathematical Finance 2010 205–266 (Lecture Notes in Mathematics Vol. 2003, Springer, 2011).

  58. Liu, D., Wang, D., Wang, F., Li, H. & Yang, X. Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems. IEEE Trans. Cybern. 44, 2834–2847 (2014).

    Article  Google Scholar 

  59. Lee, J. H., Park, J., Bennis, M. & Ko, Y. C. Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks. In Proc. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 1–6 (IEEE, 2020).

  60. Lee, J.-H., Park, J., Bennis, M. & Ko, Y.-C. Integrating LEO satellites and multi-UAV reinforcement learning for hybrid FSO/RF non-terrestrial networks. Preprint at https://arxiv.org/abs/2010.10138 (2020).

  61. Shiri, H., Park, J. & Bennis, M. Communication-efficient massive UAV online path control: federated learning meets mean-field game theory. IEEE Trans. Commun. 68, 6840–6857 (2020).

    Article  Google Scholar 

  62. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proc. 34th International Conference on Machine Learning Vol. 70 (eds Precup, D. & Teh, Y. W.) 1126–1135 (PMLR, 2017); http://proceedings.mlr.press/v70/finn17a.html

  63. Frankle, J. & Carbin, M. The lottery ticket hypothesis: finding sparse, trainable neural networks. In Proc. 7th International Conference on Learning Representations (ICLR 2019) (OpenReview.net, 2019); https://openreview.net/forum?id=rJl-b3RcF7

  64. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    MathSciNet  Article  Google Scholar 

  65. Samarakoon, S., Park, J. & Bennis, M. Robust reconfigurable intelligent surfaces via invariant risk and causal representations. In Proc. 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 301–305 (IEEE, 2021).

  66. Basar, E. & Yildirim, I. SimRIS channel simulator for reconfigurable intelligent surface-empowered mmWave communication systems. In Proc. 2020 IEEE Latin-American Conference on Communications (LATINCOM), 1–6 (IEEE, 2020).

  67. Sabella, R., Thuelig, A., Carrozza, M. C. & Ippolito, M. Industrial automation enabled by robotics, machine intelligence and 5G. Ericsson Technol. Rev. 2018, 1–13 (2018).

    Google Scholar 

  68. Kim, K. S. et al. Ultrareliable and low-latency communication techniques for tactile internet services. Proc. IEEE 107, 376–393 (2019).

    Article  Google Scholar 

  69. 5G; Service Requirements for Next Generation New Services and Markets Technical Report 22.261 Rel-15 (3GPP, 2018).

Download references

Acknowledgements

This research was supported in part by EU-CHISTERA project LeadingEdge, CONNECT and 6G Flagship (6GENESIS), and in part by JSPS KAKENHI grant numbers JP17H03266 and JP18K13757.

Author information

Authors and Affiliations

Authors

Contributions

M.B., J.P. and S.S. conceived the work and wrote the manuscript. T.N., A.E., H.S. and M.K.A.-A. carried out the use-case investigations.

Corresponding authors

Correspondence to Jihong Park, Sumudu Samarakoon, Hamid Shiri, Mohamed K. Abdel-Aziz, Takayuki Nishio, Anis Elgabli or Mehdi Bennis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Zhiguo Ding and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Park, J., Samarakoon, S., Shiri, H. et al. Extreme ultra-reliable and low-latency communication. Nat Electron 5, 133–141 (2022). https://doi.org/10.1038/s41928-022-00728-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-022-00728-8

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing