Extreme URLLC: Vision, Challenges, and Key Enablers

By Jihong Park, Sumudu Samarakoon, Hamid Shiri, Mohamed K. Abdel-Aziz, Takayuki Nishio, Anis Elgabli, Medhi Bennis. 2020

109 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, methodology): “Split learning has been adopted in Internet of Things (IOT) and edgedevice machine learning settings in [20, 21, 22, 23] including multi-modal fusion based machine learning across edge devices in [24, 25] with data collected at the edge on imagery and millimeter wave (mmWave) radio frequency (RF) si”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “Such a trend has been put forward in [282] and is referred to as extreme URLLCs.”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “By leveraging ML at the network edge, each edge node can proactively carry out decision-making based on its local predictions, thereby experiencing zero-latency [2], [17].”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, methodology): “With FL, multiple edge computing units can be used to cooperatively learn a shared network model, which can decrease service delay and provide high reliability [38,39].”
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning (2019, unknown): “…the sheer amount of these user-generated private data is instrumental in training high-accuracy machine learning (ML) models in various domains, ranging from medical diagnosis and disaster/epidemic forecast [1] to ultra-reliable and low latency communication (URLLC) and control systems [2]– [4].”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, background): “gies in the 6G era, lower radio latencies of the order of 100 μs at Gbits/s data rates are required to enable extreme networking solutions [18, 58].”
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM (2020, background): “These applications commonly require extremely low latency and high reliability while accurately reacting to local environmental dynamics (Park et al., 2020b).”