Extreme URLLC: Vision, Challenges, and Key Enablers
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Abstract (arXiv)
Notwithstanding the significant traction gained by ultra-reliable and low-latency communication (URLLC) in both academia and 3GPP standardization, fundamentals of URLLC remain elusive. Meanwhile, new immersive and high-stake control applications with much stricter reliability, latency and scalability requirements are posing unprecedented challenges in terms of system design and algorithmic solutions. This article aspires at providing a fresh and in-depth look into URLLC by first examining the limitations of 5G URLLC, and putting forward key research directions for the next generation of URLLC, coined eXtreme ultra-reliable and low-latency communication (xURLLC). xURLLC is underpinned by three core concepts: (1) it leverages recent advances in machine learning (ML) for faster and reliable data-driven predictions; (2) it fuses both radio frequency (RF) and non-RF modalities for modeling and combating rare events without sacrificing spectral efficiency; and (3) it underscores the much needed joint communication and control co-design, as opposed to the communication-centric 5G URLLC. The intent of this article is to spearhead beyond-5G/6G mission-critical applications by laying out a holistic vision of xURLLC, its research challenges and enabling technologies, while providing key insights grounded in selected use cases.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jan 2020.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 1.
7 papers.
- 2022Generative Joint Source-Channel Coding for Semantic Image Transmissioncited by 58
- 2021Attention-based Reinforcement Learning for Real-Time UAV Semantic Communicationcited by 14
- 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trendscited by 8
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
- 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Predictioncited by 1
- 2021Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representationscited by 1
- 2024Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deploymentcited by 0
- 2024Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment29 Oct 2024
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights7 Dec 2023
- 2022Generative Joint Source-Channel Coding for Semantic Image Transmission24 Nov 2022
- 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends23 Aug 2022
- 2021Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication22 May 2021
- 2021Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations4 May 2021
- 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction16 Apr 2021