Real Time Convex Optimisation for 5G Networks and Beyond (Telecommunications)

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 About the Authors 1 Convexity and convex optimisation problems 1.1 Convex sets 1.2 Convex functions 1.3 Convex optimisation problems 2 Recognition and classification of convex programming 2.1 Relation to definition 2.2 Relation to derivatives 2.2.1 First-order conditions 2.2.2 Second-order conditions 2.3 Relation to convexity propositions 2.4 Relation to classes of convex programming 2.4.1 Linear programming 2.4.2 Quadratic programming 2.4.3 Second-order cone programming 2.4.4 Geometric programming 2.4.5 Semidefinite programming 2.5 Relation to equality and inequality 3 Convex optimisation for signal processing and wireless communication 3.1 Convex optimisation for signal estimation 3.2 Convex optimisation for resource allocation problems 3.3 Convex optimisation for the problems of scheduling and deployment in wireless networks 3.4 Convex optimisation for emerging wireless network technologies 3.5 Convex optimisation for smart wireless networks 4 Introduction to real-time embedded optimisation programming 4.1 Concepts of real-time systems 4.1.1 Modelling real-time systems 4.1.2 Real-time dynamic scheduling 4.1.3 Real-time communication 4.1.4 Real-time performance analysis 4.2 Real-time computing 4.3 Real-time embedded systems 4.4 Real-time embedded convex optimisation 4.4.1 Disciplined convex programming 4.4.2 Code generation 5 Introduction to practical optimisation problems 5.1 Stochastic optimisation 5.1.1 Analysis of stochastic optimisation 5.1.2 Characteristics of stochastic optimisation 5.1.3 Popular stochastic algorithms 5.1.4 Stochastic optimisation in wireless communication systems 5.2 Large-scale optimisation 5.2.1 Large-scale unconstrained optimisation 5.2.2 Large-scale constrained optimisation 5.2.3 Large-scale optimisation in the wake of big data 5.2.4 Examples of large-scale optimisation 5.3 Multi-objective optimisation 5.3.1 Definition of multi-objective optimisation 5.3.2 Example of multi-objective optimisation 5.4 Integer programming and combinatorial optimisation 5.4.1 Branch-and-bound methods 5.4.2 Dynamic programming 5.5 Real-time optimisation problems 5.6 Introduction to methodologies of real-time optimisation 6 First-order methods for real-time optimisation 6.1 An overview of first-order methods 6.2 Accelerated first-order approaches 6.3 Proximal methods for non-smooth problems 6.4 Stochastic gradient methods 6.5 Applications of first-order optimisation in 5G IoT 7 Distributed and parallel computing for real-time optimisation 7.1 Introduction to parallel computing 7.2 The role of parallel computing in optimisation 7.3 Parallel first-order optimisation approaches 7.4 Alternating direction method of multipliers 7.5 Applications of parallel computing in 5G Internet of Things 8 Machine learning for real-time optimisation 8.1 Brief overview of machine learning for wireless communication 8.2 Interplay of machine learning and optimisation 8.3 Deep neural networks 8.4 Reinforcement learning 9 Real-time embedded convex programming 9.1 Real-time operating systems and programming languages for embedded systems 9.1.1 Complexity of convex optimisation problem 9.1.2 Running time of algorithms 9.2 Embedded optimisation software 9.2.1 Embedded in MATLAB® 9.2.2 Embedded in Python programming 9.2.3 Embedded in R programming 9.2.4 Embedded in JULIA programming 10 Real-time embedded optimisation in UAV communications 10.1 Unmanned aerial vehicle networks in IoT 10.1.1 Introduction to UAV in IoT 10.1.2 Characteristics of UAV networks 3D location Channel model Free-space model Routing and trajectory design Model predictive control 10.1.3 Design and management of UAV systems 11 An introduction of real-time embedded optimisation programming for UAV systems 11.1 Introduction 11.2 UAV-enabled communication networks 11.2.1 Challenges of UAV-enabled communications 11.2.2 Practical embedded optimisation programming for UAV systems 11.3 Practical applications for embedded optimisation in UAV systems 11.4 Conclusions 12 Real-time optimal resource allocation for embedded UAV communication systems 12.1 Introduction 12.2 Problem statement 12.3 Joint harvesting time and power allocation for EE maximisation 12.4 Near-optimal resource allocation algorithms for EE maximisation 12.4.1 Optimal power allocation 12.4.2 Optimal harvesting time 12.5 Implementation 12.6 Conclusion 13 Real-time deployment and resource allocation for distributed UAV systems in disaster relief 13.1 Introduction 13.2 System model and problem formulation 13.2.1 System model 13.2.2 Problem formulation 13.3 Constrained K-means clustering method 13.3.1 Preliminaries of K-means method 13.3.2 Clustering model with QoS constraints 13.3.3 Selecting the number of clusters 13.4 Maximising end-to-end throughput via distributed power allocation 13.5 Simulation results 13.6 Conclusions 14 Practical optimisation of path planning and completion time of data collection for UAV-enabled disaster communications 14.1 Introduction 14.2 UAV-WSN system model 14.2.1 System model 14.2.2 Sensing data 14.2.3 Problem formulation 14.3 Optimal completion time by peer-to-peer UAV-GS networks 14.3.1 Estimating the number of UAVs 14.3.2 Proposed optimisation problem and solving approach 14.4 Optimal completion time by clustering UAV-GS networks (CUN) 14.4.1 Constrained K-means clustering model 14.4.2 Proposed solving approach 14.5 Simulation results 14.6 Conclusions 15 Learning-aided real-time performance optimisation of cognitive UAV-assisted disaster communication 15.1 Introduction 15.2 UAV-CRN system and channel model 15.2.1 System model 15.2.2 Channel model 15.2.3 Transmission scheme 15.2.4 Problem formulation 15.3 Learning optimisation for a real-time scenario of UAV deployment 15.3.1 Conventional optimisation approach for UAV deployment 15.3.2 Deep neural network for learning optimisation of UAV deployment 15.4 Maximising EE performance via robust power allocation 15.5 Simulation results 15.6 Conclusions References Appendices A.1 Appendix A A.1.1 Basic vector and matrix calculation Product of vector and matrix The definition of unitary matrix Kronecker product: A.1.2 Matrix Norm A.1.3 Logarithm of positive definite matrices Logarithm of Kronecker product: A.1.4 Trace and logarithm relationship A.1.5 Some case studies of complex matrices A.1.6 Schur complement A.1.7 Matrix Inverse Analysis Sherman Morrison Lemma Woodbury matrix identity: Sherman-Morrison-Woodbury Jensen’s inequality Cauchy-Schwarz inequality: A.1.8 Sum of matrices inversion A.1.9 Inversion Identities A.1.10 Determination inversion A.1.11 Log-determinant of a matrix (log det(.)) A.2 Appendix B A.2.1 Some equalities and inequalities in R A.2.2 Some norm inequalities of square matrix in Rnxn A.3 Appendix C A.3.1 Some inequalities using first-order approximation for determining lower bound of complex functions A.3.2 Some inequalities using first-order approximation for determining upper bound of complex functions A.3.3 Some logarithm inequalities using first-order approximations Notation Some specific sets Vectors and matrices Algebra calculators Functions and derivatives Index Back Cover