Ultra-reliable and low-latency communications (URLLC) theory and practice : advances in 5G and beyond / edited by Trung Q. Duong, Saeed R. Khosravirad, Changyang She, Petar Popovski, Mehdi Bennis, and Tony Q.S. Quek.

Includes bibliographical references and index.

Table of Contents
Preface vii

List of Contributors ix

1 URLLC: Faster, Higher, Stronger, and Together 1
Changyang She, Trung Q. Duong, Saeed R. Khosravirad, Petar Popovski, Mehdi Bennis, and Tony Q.S. Quek

2 Statistical Characterization of URLLC: Frequentist and Bayesian Approaches 15
Tobias Kallehauge, Pablo Ramirez-Espinosa, Anders E. Kalør, and Petar Popovski

3 Characterizing and Taming the Tail in URLLC 61
Chen-Feng Liu, Yung-Lin Hsu, Mehdi Bennis, and Hung-Yu Wei

4 Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints 85
Chengjian Sun, Changyang She, and Chenyang Yang

5 Channel Coding and Decoding Schemes for URLLC 119
Chentao Yue, Mahyar Shirvanimoghaddam, Branka Vucetic, and Yonghui li

6 Sparse Vector Coding for Ultra-reliable and Low-latency Communications 169
Byonghyo Shim

7 Network Slicing for URLLC 215
Peng Yang, Xing Xi, Tony Q. S. Quek, Jingxuan Chen, Xianbin Cao, and Dapeng Wu

8 Beamforming Design for Multi-user Downlink OFDMA-URLLC Systems 241
Walid R. Ghanem, Vahid Jamali, Yan Sun, and Robert Schober

9 A Full-Duplex Relay System for URLLC with Adaptive Self-Interference Processing 259
Hanjun Duan, Yufei Jiang, Xu Zhu, and Fu-Chun Zheng

10 Mobility Prediction for Reducing End-to-End Delay in URLLC 291
Zhanwei Hou, Changyang She, Yonghui Li, and Branka Vucetic

11 Relay Robot-Aided URLLC in 5G Factory Automation with Industrial IoTs 321
Dang Van Huynh, Saeed R. Khosravirad, Yuexing Peng, Antonino Masaracchia, and Trung Q. Duong

Index 343

Available to OhioLINK libraries.

"Pursuing ever higher data rates has been the central design goal in all the previous generations of mobile communications. This has been changed in the fifth generation (5G) mobile communications that aim to support various new emerging services with diverse and stringent quality-of-service requirements. The most formidable challenge in 5G is to achieve ultra-reliable low-latency communications (URLLC) for many mission-critical services including autonomous vehicles, industry automation, and tele-robotic surgery, i.e., the roundtrip delay of 1 millisecond and less than 1 out of a million in packet loss. In the fourth generation (4G) systems, the average latency is usually a few hundred milliseconds, and the packet loss probability is around 1%. 5G systems need to significantly improve the latency and reliability by several orders of magnitude compared to 4G systems. This presents unprecedented challenges. This book covers a range of topics from fundamental theories to practical solutions in URLLC. In Chapters 2 and 3, the authors analyze the statistical features and tail distributions of wireless channel and provide useful insights on the performance of URLLC. Chapter 2 presents the statistical aspects of URLLC in both frequentist and Bayesian approaches. The authors have analyzed the statistical features and guarantees for outage probability in a narrowband wireless channel. Chapter 3 considers various metrics of URLLC including tail distribution, higher-order statistics, extreme events with very low occurrence probabilities, worst-case metrics, and reliability/latency. The authors have introduced readers the entropic risk measure in financial mathematics, generalized extreme value, and generalized Pareto distribution to investigate these metrics. From Chapter 4 to Chapter 7, the authors introduced several techniques to guarantee the reliability and latency of URLLC, including machine learning, candidate channel codes, sparse vector coding, and network slicing. Two problems of resource allocation in URLLC are addressed in Chapter 4 with an unsupervised learning approach. The results have shown that bandwidth utilization efficiency of URLLC can be improved more significantly by exploiting frequency diversity than by multi-user diversity. Chapter 5 discusses the channel coding and decoding schemes for URLLC. This chapter reviews state-of-the-art channel codes for URLLC and analyzes them in terms of performance and complexity. Furthermore, the ordered statistics decoding (OSD) is promoted as one of the potential universal decoding algorithms for URLLC. In Chapter 6, a new approach to support short packet transmissions, referred as sparse vector coding (SVC) is introduced. The numerical evaluations and performance analysis validate the proposed SVC technique is highly effective in URLLC transmissions. Chapter 7 studies CoMP-enabled RAN slicing system simultaneously supporting URLLC and eMBB services. The authors address a joint bandwidth and CoMP beamforming optimization problem to maximize the long-term total slice utility. In Chapters 8 and 9, downlink orthogonal frequency division multiple access systems (OFDMA) and full-duplex relay system are optimized for URLLC, respectively. Chapter 8 investigates the beamforming design for downlink ODFMA to enable the stringent delay requirement. In particular, the authors address a non-convex optimization problem to maximize the weighted system sum throughput subject to quality-of-service (QoS) of URLLC users. Chapter 9 presents an up-to-date overview of the end-to-end latency for a full-duplex (FD) relay system in the context of URLLC. The authors not only provide an insightful investigation of reliability and latency together for FD relay assisted URLLC but also discuss possible relaying latency reduction solutions in the chapter. In Chapters 10 and 11, the authors investigate URLLC in vertical industries: Tactile Internet and Industrial Internet-of-Things. More specifically, Chapter 10 addresses an optimization problem that maximizes the number of URLLC services by jointly optimizing time and frequency resources and the prediction horizon. The numerical results clearly demonstrate the effectiveness of the proposed solution. In addition, a proof-of-concept experiment with the remote control in a virtual factory is also provided to illustrate a typical application of Tactile Internet. Finally, Chapter 11 considers relay robots-aided URLLC in 5G factory automation, which consists of multiple relay robots deployment and decoding error probability minimization problems. There are two different approaches introduced for relay robots deployment, including deep neural networks (DNN) and the K-means clustering algorithm. A low-complexity iterative algorithm is also provided to deal with the joint blocklength and power allocation problem to minimize the decoding error probability"-- Provided by publisher.

About the Author
Trung Q. Duong is a Chair Professor of Telecommunications, School of Electronics, Electrical Engineering and Computer Science at Queen’s University Belfast, UK and a Research Chair of Royal Academy of Engineering. He is a Fellow of the IEEE.

Saeed R. Khosravirad is a Member of Technical Staff at Nokia Bell Labs, Murray Hill, NJ, USA. He is currently leading research projects in Bell Labs investigating various aspects of radio access for industrial IoT.

Changyang She is a Research Associate with The University of Sydney, Australia. He is serving as the Australian Research Council, Discovery Early Career Researcher Award (DECRA) Fellow at the University of Sydney.

Petar Popovski is a Professor at Aalborg University, Denmark. He is Villum Investigator, previous holder of a Consolidator Grant (2015-2020) from the European Research Council (ERC) and a recipient of the Danish Elite Researcher Award (2016). He is a Fellow of the IEEE.

Mehdi Bennis is a Full Professor at the Centre for Wireless Communications, University of Oulu, Finland. Dr Bennis is a Specialty Chief Editor for Data Science for Communications in the Frontiers in Communications and Networks journal. He is a Fellow of the IEEE.

Tony Q.S. Quek is a Full Professor at Singapore University of Technology and Design (SUTD). He is also the Director of Future Communications R&D Programme, ISTD Pillar Head, Sector Lead of SUTD AI Program, and Deputy Director of the SUTD-ZJU IDEA. He is a Fellow of the IEEE.