Extreme URLLC: Vision, Challenges, and Key Enablers

Abstract

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.

arXiv comment7 pages, 6 figures; This work has been submitted to the IEEE for possible publication

References in corpus (1)

• Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

Cited by in corpus (19)

• AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends
• White Paper on Critical and Massive Machine Type Communication Towards 6G
• RIS-Assisted Coverage Enhancement in Millimeter-Wave Cellular Networks
• 6G White Paper on Edge Intelligence
• 6G White Paper on Machine Learning in Wireless Communication Networks
• When Wireless Communications Meet Computer Vision in Beyond 5G
• XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
• Semantics-Native Communication with Contextual Reasoning
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
• A Digital Twin for Reconfigurable Intelligent Surface Assisted Wireless Communication
• On Fading Channel Dependency Structures with a Positive Zero-Outage Capacity
• Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
• Reconfigurable Intelligent Surface: Design the Channel -- a New Opportunity for Future Wireless Networks
• Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
• Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction
• QoS-Oriented Sensing-Communication-Control Co-Design for UAV-Enabled Positioning
• Exploiting Diversity for Ultra-Reliable and Low-Latency Wireless Control
• Recursive Periodicity Shifting for Semi-Persistent Scheduling of Time-Sensitive Communication in 5G
• Predictive Ultra-Reliable Communication: A Survival Analysis Perspective