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Full Description
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.
Contents
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
