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.
Author(s): Ramona Trestian
Publisher: CRC Press
Year: 2022
Language: English
Pages: 345
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
List of Figures
List of Tables
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
1.2.1. Multimedia-Based Network Delivery Solutions
1.2.2. 360° Video Streaming Applications
1.2.3. 360° Mulsemedia Streaming Applications
1.2.4. Quality of Service (QoS) and Quality of Experience (QoE) Provisioning over Wireless Networks
1.2.5. QoS Provisioning for Multimedia Delivery
1.2.6. Approaches for Measuring the Video Quality
1.3. A Simplified Network Model
2. Wireless Transmission Fundamentals
2.1. Spectrum and Frequencies
2.2. Signals for Conveying Information
2.2.1. Basic Concepts and Terminology
2.2.2. Time and Frequency Domain
2.2.3. Baseband and Carrier
2.3. Antennas
2.3.1. Antennas for Mobile Devices
2.3.2. Sectorized Antennas for Cellular Networks
2.4. Multiplexing and Modulation
2.4.1. Multiplexing Techniques
2.4.2. Modulation Techniques
2.5. Spread Spectrum
2.6. Medium Access Mechanisms
2.6.1. Fixed-Assignment Access Solutions
2.6.2. Random Access Solutions
2.7. Practical Use-Case Scenario: Antennas Using Altair WinProp
2.7.1. Creation of Urban Database Using WallMan
2.7.2. Produce 3D Antenna Pattern Using AMan
2.7.3. Urban Simulation Using ProMan
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
3.12.1. Creation of Urban Database Using WallMan
3.12.2. Urban Simulation Using ProMan
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
4.2.1. Worked Example 01
4.2.2. Worked Example 02
4.2.3. Worked Example 03
4.2.4. Worked Example 04
4.3. Mobility Management and Handover
4.4. Evolution from 1G to 5G and Beyond
4.4.1. First Generation – 1G
4.4.2. Second Generation – 2G
4.4.3. 2.5 Generation – 2.5G
4.4.4. Third Generation – 3G
4.4.5. Fourth Generation – 4G
4.4.6. Fifth Generation – 5G
4.4.6.1. AI-Based Resource Allocation
4.4.6.2. Network Slicing
4.4.6.3. Software-Defined Networking
4.4.6.4. Network Function Virtualization
4.4.6.5. Multi-Access Edge Computing
4.4.7. Sixth Generation – 6G
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
5.2.1. GEO Satellites
5.2.2. MEO Satellites
5.2.3. LEO Satellites
5.3. Applications of Satellites
5.3.1. Iridium
5.3.2. Globalstar
5.3.3. Inmarsat
5.3.4. Starlink
5.3.5. Global Positioning System
5.4. Routing and Localization
5.5. Practical Use-Case Scenario: Satellite Communications Using Altair WinProp
5.5.1. Geostationary Communication Satellite for Rural/Suburban Coverage
5.5.2. Geostationary Communication Satellite for Urban Coverage
5.5.3. GPS Satellites System for Urban Coverage
6. Wireless Evolution
6.1. Wireless Technologies Evolution
6.2. Mobile Ad-Hoc Networks
6.2.1. Proactive Routing Protocols
6.2.2. Reactive Routing Protocols
6.2.3. Hybrid Routing Protocols
6.3. Vehicular Networks
6.3.1. Wireless-Based Vehicular Communications
6.3.2. Cellular-Based Vehicular Communication
6.4. Millimeter Wave Multi-Gigabit Wireless Networks
6.5. Use-Case Scenarios: Trends in Heterogeneous Environments Integration
6.5.1. 5GonWheels
6.5.2. I-RED
6.5.3. PLATON
6.6. Practical Use-Case Scenario: Wireless Indoor Communication Using Altair WinProp
III. Paradigms of Intelligent Networked Systems
7. Intelligent Environments and Internet of Things
7.1. IoT Life-Cycle
7.2. IoT Applications
7.2.1. Smart Surveillance Systems
7.2.2. Autonomous Vehicles
7.2.3. Healthcare
7.2.4. Virtual Reality
7.2.5. eLearning
7.2.6. Smart Agriculture
7.3. Wireless Access Networks for IoT
7.3.1. LoRa and LoRaWAN
7.3.2. ZigBee
7.3.3. IEEE 802.11ah
7.3.4. Bluetooth
7.4. Introduction to Machine Learning for IoT
7.4.1. Linear Regression
7.4.2. K-Nearest Neighbours
7.4.3. Decision Tree
7.4.4. Random Forest
7.4.5. Naive Bayes
7.4.6. Logistic Regression
7.4.7. Cross-Validation
7.4.8. Bias and Variance
7.4.9. Confusion Matrix
7.5. Digital Twins for Industrial IoT
7.5.1. DT for Manufacturing
7.5.2. DT for Next Generation Networks
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, Jupyter
7.7.1. Use-Case Scenario Settings
7.7.2. Data Sets Summary
7.7.3. Source Code and Results
7.8. Practical Use-Case Scenario: ML for Smart Cities IoT Using Python, Tensorflow, Jupyter
7.8.1. Use-Case Scenario Settings
7.8.2. Data Set Summary
7.8.3. Source Code and Results
List of Acronyms
Index
