Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

By Medhi Bennis, Senior Member, Merouane Debbah, H. Vincent Poor. 2018

937 citations recorded by the source corpus.

Ensuring ultrareliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay, and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture and topology (across access, edge, and core), and decision-making under uncertainty is sorely lacking. The overarching goal of this paper is a first step to filling this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a wide variety of techniques and methodologies pertaining to the requirements of URLLC, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliability wireless networks.

Subfields: Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “In essence, URLLC warrants a departure from average-based system design towards a cleanslate design centered on tail, risk, and scale [5].”
Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “Therefore, some of the vehicular users (VUEs) may experience unacceptable latencies yielding degraded performance [11].”
One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction (2019, background): “60468.3368176 1 INTRODUCTION Received power prediction of radio frequency (RF) signals in millimeter wave (mmWave) is a key enabler for proactive 5G cellular system operations, and is a daunting task [1]. These signals are sensitive to physical blockages, and the received power levels drop significa”
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020, background): “Therefore, the objective of eMBB service is to achieve high data rate while satisfying a moderate reliability with packet error rate (PER) of 10−3 [1], [2].”
A Statistical Learning Approach to Ultra-Reliable Low Latency Communication (2018, background): “Within the context established in [5], an even earlier work by Eggers et al.”
Extreme URLLC: Vision, Challenges, and Key Enablers (2020, methodology): “999% (5-nine) reliability and 1 ms latency targets, using a plethora of techniques (short packet transmission, grant-free mechanisms, leveraging spatial, frequency, and temporal diversity techniques [1], [3], [4]).”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “This probability ranges from 10 − 3 to 10 − 9 , depending on the mission-critical application in 5G networks [69].”
URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems (2018, background): “Haptic traffic, by contrast, should guarantee a fixed target rate and high reliability with PER on the order of 10−4 ∼ 10−5 [7], [8], which can be satisfied via ultra-reliable and low latency communication (URLLC) links [9], [10].”
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems (2019, background): “Recent works in [7] and [12] provide a first step in this direction.”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “Some recent research in [74], [75] provides the first step toward building highperformance wireless networks to support next-generation Internet services.”
Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018, background): “More sophisticated multiple access schemes with reliability guarantees, as done in [1], [2], [16], are applicable with additional complexity.”
Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC) (2018, background): “A different perspective is taken in [14], where, ﬁrst, relevant metrics and key enables of URLLC are discussed, where after mathematical tools from different scientiﬁc disciplines are proposed for evaluating the URLLC properties of different wireless communication system applications.”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “tions, which mandates a further examination in terms of bound violation probability, high-order statistics, characterization of the extreme events with very low occurrence probabilities, and so forth [24]. The main contribution of this work is to propose a URLLC-centric task ofﬂoading and resource a”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “The pursuit of extremely stringent latency and reliability guarantees is essential in the fifth generation (5G) communication system and beyond [1], [2].”
Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks (2019, background): “ate the consensus based FL tool, for both CNN and 2NN models, over networks with increasing number of devices from Conﬁguration (1) Conﬁguration (2) Conﬁguration (3) K<15 jN kj6 K2[15;50] jN kj2[2;6] K>50 jNkj= 2 CFA t= 1 t = 0:025 k;i in (10) = 0:5 t = 0:025 k;i in (10) = 0:5 t = 0:025 k;i”
Reconfigurable Intelligent Surfaces for Localization: Position and Orientation Error Bounds (2020, background): “eration mobile networks (6G) promote continuous connection availability, strong reliability, huge device density (107 devices per km2) and air interface latency of sub-milliseconds (e.g., 10µs), etc. [3], [4]. To meet these requirements, reconﬁgurable intelligent surfaces (RISs) might represent a ke”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, background): “Indeed, achieving ultra-reliable low-latency communication (URLLC) represents one of the major challenges facing 5G and vehicular networks [5].”