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Extreme ultra-reliable and low-latency communication


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.

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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.


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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.

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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.

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

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

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Park, J., Samarakoon, S., Shiri, H. et al. Extreme ultra-reliable and low-latency communication. Nat Electron 5, 133–141 (2022).

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