Real time convex optimisation for 5G networks and beyond Long D. Nguyen, Trung Q. Duong, and Hoang Duong Tuan

Format:

Book

Author/Creator:

Nguyen, Long D., author.

Duong, Trung Q., author.

Hoang, Tuan D., author.

Series:

IET telecommunications series 87

Language:

English

Subjects (All):

5G mobile communication systems.

Mobile communication systems--Technological innovations.

Mobile communication systems.

Real-time data processing.

Physical Description:

1 online resource

Place of Publication:

London The Institution of Engineering and Technology 2021

Summary:

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

Contents:

Intro

Halftitle Page

Series Page

Title Page

Copyright

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

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

A.1.2 Matrix Norm

A.1.3 Logarithm of positive definite matrices

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

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 (det(.))

A.2 Appendix B

A.2.1 Some equalities and inequalities in ℝ

A.2.2 Some norm inequalities of square matrix in ℝn×n

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

Index

Back Cover

Notes:

Includes bibliographical references and index

Description based on print version record; title from online resource (viewed July 11, 2022)

Other Format:

Print version Nguyen, Long D. Real Time Convex Optimisation for 5G Networks and Beyond

ISBN:

1785619608

9781785619601

OCLC:

1287134989

Access Restriction:

Restricted for use by site license