Real Time Convex Optimisation for 5G Networks and Beyond (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
223
Utgivningsdatum
2022-02-11
Förlag
Institution of Engineering and Technology
Dimensioner
239 x 163 x 15 mm
Vikt
522 g
Antal komponenter
1
ISBN
9781785619595

Real Time Convex Optimisation for 5G Networks and Beyond

av Long D Nguyen, Trung Q Duong, Hoang D Tuan
Inbunden,  Engelska, 2022-02-11
1845
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There is no doubt that we are facing a wireless data explosion. Modern wireless networks need to satisfy increasing demand, but are faced with challenges such as limited spectrum, expensive resources, green communication requirements and security issues. In the age of internet of things (IoT) with massive data transfers and huge numbers of connected devices, including high-demand QoS (4G, 5G networks and beyond), signal processing is producing data sets at the gigabyte and terabyte scales. Modest-sized optimisation problems can be handled by online algorithms with fast speed processing and a huge amount of computer memory. With the rapid increase in powerful computers, more efficient algorithms and advanced parallel computing promise an enormous reduction in calculation time, solving modern optimisation problems on strict deadlines at microsecond or millisecond time scales. Finally, the interplay between machine learning and optimisation is an efficient and practical approach to optimisation in real-time applications. Real-time optimisation is becoming a reality in signal processing and wireless networks. This book considers advanced real-time optimisation methods for 5G and beyond networks. The authors discuss the fundamentals, technologies, practical questions and challenges around real-time optimisation of 5G and beyond communications, providing insights into relevant theories, models and techniques. The book should benefit a wide audience of researchers, practitioners, scientists, professors and advanced students in engineering, computer science, ubiquitous computing, information technology, and networking and communications engineering, as well as professionals in government agencies.
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Long D Nguyen, Trung Q Duong, Hoang D Tuan

Övrig information

Long D. Nguyen is a lecturer at Dong Nai University and adjunct assistant Professor at Duy Tan University, Vietnam. His research interests include convex optimisation techniques for resource management in wireless communications, energy efficiency approaches for 5G networks (heterogeneous networks, relay networks, cell-free networks, and massive MIMO) and real-time optimisation for wireless communication networks and Internet of Things. He holds a PhD in Electronics and Electrical Engineering from Queen's University Belfast, UK. Trung Q. Duong is a professor at Queen's University Belfast, UK, and a Research Chair of Royal Academy of Engineering. His research interests include wireless communications, signal processing, machine learning and optimisation for wireless networks. He serves as editor for IEEE Trans on Wireless Communications and executive editor for IEEE Communications Letters. He received the Royal Academy of Engineering Research Fellowship (2016-2020) and won the Newton Prize in 2017. He is co-editor of the IET book Trusted Communications with Physical Layer Security. Hoang D. Tuan is a professor at the School of Electrical and Data Engineering, University of Technology Sydney, Australia. He has been involved in research on optimisation, control, signal processing, wireless communication, and biomedical engineering for more than 20 years. He received his PhD in Applied Mathematics from Odessa State University, Ukraine.

Innehållsförteckning

Chapter 1: Convexity and convex optimisation problems Chapter 2: Recognition and classification of convex programming Chapter 3: Convex optimisation for signal processing and wireless communication Chapter 4: Introduction to real-time embedded optimisation programming Chapter 5: Introduction to practical optimisation problems Chapter 6: First-order methods for real-time optimisation Chapter 7: Distributed and parallel computing for real-time optimisation Chapter 8: Machine learning for real-time optimisation Chapter 9: Real-time embedded convex programming Chapter 10: Real-time embedded optimisation in UAV communications Chapter 11: An introduction of real-time embedded optimisation programming for UAV systems Chapter 12: Real-time optimal resource allocation for embedded UAV communication systems Chapter 13: Real-time deployment and resource allocation for distributed UAV systems in disaster relief Chapter 14: Practical optimisation of path planning and completion time of data collection for UAV-enabled disaster communications Chapter 15: Learning-aided real-time performance optimisation of cognitive UAV-assisted disaster communication References Appendices

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