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.

• 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