1st Edition
Mobile and Wireless Communications with Practical Use-Case Scenarios
The growing popularity of advanced multimedia-rich applications along with the increasing affordability of high-end smart mobile devices has led to a massive growth in mobile data traffic that puts significant pressure on the underlying network technology. However, no single network technology will be equipped to deal with this explosion of mobile data traffic. While wireless technologies had a spectacular evolution over the past years, the present trend is to adopt a global heterogeneous network of shared standards that enables the provisioning of quality of service and quality of experience to the end-user. To this end, enabling technologies like machine learning, Internet of Things and digital twins are seen as promising solutions for next generation networks that will enable an intelligent adaptive interconnected environment with support for prediction and decision making so that the heterogeneous applications and users' requirements can be highly satisfied.
The aim of this textbook is to provide the readers with a comprehensive technical foundation of the mobile communication systems and wireless network design, and operations and applications of various radio access technologies. Additionally, it also introduces the reader to the latest advancements in technologies in terms of Internet of Things ecosystems, machine learning and digital twins for IoT-enabled intelligent environments. Furthermore, this textbook also includes practical use-case scenarios using Altair WinProp Software as well as Python, TensorFlow and Jupyter as support for practice-based laboratory sessions.
I. Fundamental Aspects of Signals, Analogue and Digital Communication Systems. 1. The Wireless Vision. 1.1 Introduction to wireless communication - evolution and history. 1.2 Applications and Technical Challenges. 1.3 A simplified network model. 2. Wireless Transmission Fundamentals. 2.1 Spectrum and frequencies. 2.2 Signals for conveying information. 2.3 Antennas. 2.4 Multiplexing and modulation. 2.5 Spread Spectrum. 2.6 Medium Access Mechanisms. 2.7 Practical Use-Case Scenario: Antennas using Altair WinProp. 3. Radio Propagation. 3.1 Introduction to Signal Propagation. 3.2 Multi-Path Propagation. 3.3 Fresnel Zone. 3.4 Path Loss and Path Loss Models. 3.5 Free Space Propagation Model. 3.6 Two Ray Ground Model. 3.7 Okumura Model. 3.8 Okumura-Hata Model. 3.9 COST 231 Walfisch Ikegami. 3.10 Intelligent Ray Tracing. 3.11 Dominant Path Model. 3.12 Practical Use-Case Scenario: Radio Propagation using Altair WinProp. 3.13 Practical Use-Case Scenario: Rural/Suburban Study using Altair WinProp.
II. Evolution of Mobile and Wireless Systems. 4 The Cellular Concept and Evolution. 4.1 Cellular Systems Fundamentals. 4.2 Traffic Engineering in Cellular Systems – Problem Solving. 4.3 Mobility Management and Handover. 4.4 Evolution from 1G to 5G and Beyond. 4.5 Practical Use-Case Scenario: Network Planning for urban scenarios using LTE with Altair WinProp. 4.6 Practical Use-Case Scenario: 5G Network Planning with Altair WinProp. 5. Satellite Communications. 5.1 The Future of Satellite Communications. 5.2 Satellite Basics. 5.3 Applications of Satellites. 5.4 Routing and Localization. 5.5 Practical Use-Case Scenario: Satellite Communications using Altair WinProp. 6. Wireless Evolution. 6.1 Wireless Technologies Evolution. 6.2 Mobile Ad-Hoc Networks. 6.3 Vehicular Networks. 6.4 Millimeter Wave Multi Gigabit Wireless Networks. 6.5 Use-Case Scenarios: Trends in Heterogeneous Environments Integration. 6.6 Practical Use-Case Scenario: Wireless Indoor Communication using Altair WinProp.
III. Paradigms of Intelligent-based Networked Systems. 7. Intelligent Environments and Internet of Things. 7.1 IoT Life-cycle. 7.2 IoT Applications. 7.3 Wireless Access Networks for IoT. 7.4 Introduction to Machine Learning for IoT. 7.5 Digital Twins for Industrial IoT. 7.6 Use-Case Scenario: Technology for Public Health Emergencies. 7.7 Practical Use-Case Scenario: ML for Predictive Maintenance and IoT using Python, Tensorflow, Jupiter. 7.8 Practical Use-Case Scenario: ML for Smart Cities IoT using Python, Tensorflow, Jupiter.
List of Acronyms. Index.
Biography
Dr. Ramona Trestian is a Senior Lecturer with the Design Engineering and Mathematics Department, Faculty of Science and Technology, Middlesex University, London, UK. She was previously an IBM-IRCSET Exascale Postdoctoral Researcher with the Performance Engineering Laboratory (PEL) at Dublin City University (DCU), Ireland. She was awarded the PhD from Dublin City University in March 2012 and the B.Eng. in Telecommunications from Technical University of Cluj-Napoca, Romania, 2007. She has published in prestigious international conferences and journals and has five edited books. Her research interests include mobile and wireless communications, quality of experience, multimedia systems, Industry 4.0 and digital twin modelling. She is an Associate Editor of the IEEE Communications Surveys and Tutorials.
