Quantum Computing and Machine Learning for 6G
Description
Secure your expertise in the next frontier of wireless technology with this essential book, which provides a deep dive into the integration of machine learning and quantum computing to build the necessary infrastructure for 6G communication networks.
Despite the potential benefits of 6G, the technology to enable its realization is not yet available. As a result, the development of technology to solve these challenges must be met before we can start working towards 6G. The primary applications of machine learning within 6G are to create necessary infrastructure advantages as the technology matures. Additionally, 6G communication networks use quantum computing to detect, mitigate, and prevent security vulnerabilities. By integrating machine learning and quantum computing into 5G and 6G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This book highlights the role of real-time network learning and the integration of quantum computing, machine learning, and quantum machine learning to enhance service quality. It provides a deep dive into the interplay of these technologies within 6G networks, starting from 5G fundamentals. The book elaborates on how these advanced technologies will underpin 6G's architecture to meet comprehensive service demands, including those for smart city applications requiring extensive coverage, ultra-low latency, and reliable connectivity. The book details how the synergy between quantum computing, machine learning, and 6G technologies will transform communications, revolutionize markets, and enable groundbreaking applications globally.
Readers will find the volume:
- Explores real-world scenarios for illustrating the integration of quantum computing and machine learning in 6G;
- Covers an extensive range of applications to illustrate the full picture of 6G that implements machine learning and quantum computing approaches;
- Offers expert insights through a comprehensive collection of literature reviews and research articles;
- Introduces the interdisciplinary innovations and potential of 6G across multiple industries.
Audience
Scientists, industry professionals, researchers, academicians, instructors, and students working in quantum computing and machine learning, especially in the context of advanced wireless communication technology.
More details
Other editions
Additional editions
Persons
Pallavi Sapkale, PhD is an Assistant Professor, Ramrao Adik Institute of Technology, D.Y. Patil University, Navi Mumbai, Maharashtra, India, with more than 17 years of experience. She has published four books, more than 25 research articles in various international journals and conferences, four international patents, and 12 Indian patents. Her research focuses on quantum computing, machine learning, wireless communication, 5G mobility management, and next-generation networks like 6G.
Shilpa Mehta, PhD is a Teaching Assistant at the Auckland University of Technology, New Zealand, with more than five years of teaching experience. She has worked on various interdisciplinary research projects and edited several internationally published books. Her research interests include radio frequency integrated circuits, RF front ends, optimization, Internet of Things, wireless communication, artificial intelligence, healthcare, radars, and smart cities.
S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs, Coimbatore, Tamilnadu, India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. He is also the Vice-Chairman of Renewable Energy Society of India (RESI). He also serves as a research consultant for many companies, startups, and micro-, small, and medium enterprises.
Content
Preface xix
Acknowledgement xxiii
Part I: Introduction 1
1 Introduction to Wireless Communication and Transition from 1G to 6G 3
Krupali Dhawale, Pranali Bhope, Kunika Dhapodkar and Sejal Kumbhare
1.1 Introduction to Wireless Communication 4
1.1.1 Definition and Importance of Wireless Communication 4
Importance of Wireless Communications 4
1.1.2 Role of Wireless Communication in Connecting People and Devices Globally 5
1.1.3 Evolution of Wireless Communication Technologies 6
1.2 Generations of Wireless Communication 8
1.2.1 1G (Analog Cellular) 8
1.2.2 2G (Digital Cellular) 9
1.2.3 3G (Mobile Broadband) 10
1.2.4 4G (LTE and Beyond) 11
1.2.5 5G (Next-Generation Connectivity) 12
1.2.6 Anticipating 6G (Future Evolution) 14
1.3 1G to 4G: Evolution of Wireless Standards 15
1.3.1 Overview of 1G to 4G Transitions 15
1.3.2 Advancements in Digital Modulation and Compression 17
1.3.3 Shift from Analog to Digital Transmission 19
1.3.4 Introduction of Data Services and Mobile Internet 20
1.4 Industry and Research Initiatives for 6G 22
1.4.1 Involvement of Academia, Industry, and Standardization Bodies 22
1.4.2 Research Goals and Technological Roadmaps 24
Conclusion 27
References 27
2 The State-of-the-Art and Future Visioning 6G Wireless Network 29
Payal Bansal
2.1 Introduction 30
2.1.1 Heterogeneous Wireless Networks 31
2.1.2 Vertical Handover 32
2.2 Handover Management in 6G 34
2.2.1 History of Handover System 34
2.2.2 Handover Process 36
2.2.3 Single-Tier Networks with Handover Skipping Process 38
2.2.3.1 Coverage Probability 38
2.2.3.2 Handover Cost 40
2.2.3.3 Average Throughput 43
2.3 Two-Tier Network Handover Skipping 43
Bibliography 51
Part II: Quantum Computing 55
3 Introduction to Quantum Computing 57
Shilpa Mehta and Celestine Iwendi
3.1 Introduction 57
3.1.1 Historical Background 58
3.1.2 Classical Computing vs Quantum Computing 59
3.1.3 Why Quantum Computers? 60
3.1.4 Bits versus Qubits 60
3.1.5 Quantum Registers 61
3.1.6 Key Principles of Quantum Computing 61
3.2 Quantum Gates 62
3.3 Quantum Algorithms 64
3.3.1 Fourier Transform-Based Algorithms 64
3.3.1.1 Overview of Discrete Fourier Transform 65
3.3.1.2 Quantum Fourier Transform 65
3.3.2 Amplitude Amplification-Based Algorithms 68
3.3.2.1 Grover's Algorithm 68
3.3.2.2 Quantum Counting 69
3.3.3 Quantum Walk Based Algorithms 69
3.3.3.1 Boson Sampling Problem 69
3.3.3.2 Element Distinctness Problem 70
3.3.3.3 Triangle Finding Problem 70
3.3.4 Bounded-Error Quantum Polynomial Time Problems 70
3.3.4.1 Quantum Simulation 71
3.3.5 Hybrid Algorithms 71
3.3.5.1 Quantum Approximate Optimization Algorithm 71
3.3.5.2 Variational Quantum Eigensolver Algorithm 71
3.3.5.3 Contracted Quantum Eigensolver Algorithm 72
3.4 Quantum Hardware and Software 72
3.4.1 Quantum Hardware 72
3.4.1.1 Types of Quantum Hardware 73
3.4.2 Quantum Software 74
3.5 Applications 75
3.6 Challenges of Quantum Computing 78
3.7 Current State-of-the-Art 79
3.8 Summary and Future Scope 85
References 85
4 Quantum-Secured Concealed Identifier for 6G Technology 89
Pratham Desai and Dipali Kasat
Introduction 89
4.1 Quantum Mechanical Properties for Security 90
4.1.1 Entanglement with Bell-State Example 90
4.1.2 Entanglement for Bipartite System 92
4.1.3 No Cloning Theorem 93
4.2 Quantum Key Distribution Technique (QKD) 96
4.3 BB84 Algorithm 96
4.4 Concept of Identifiers 97
4.5 Drawbacks of Classical Algorithms 99
4.6 Quantum Concealed Identifiers for 6G Technology 100
4.6.1 QKD Protocol with a Multiple Coding Basis 100
4.6.2 Parameters and Basic Equipment 102
4.6.3 Pseudo-Random Number Seed Key Construction Protocol for Security (PRNSKC) 104
4.7 A Post-Quantum SUCI for 6G 105
4.7.1 How SUCI is Vulnerable to Quantum Attacks 105
4.7.2 Post-Quantum Secure SUCI 106
4.7.3 Selecting the Perfect KEM for KEMSUCI 107
4.7.4 Understanding the Kyber Algorithm 109
4.8 Comparison Between the Existing Schemes 111
Conclusion 112
Bibliography 113
5 Quantum Cryptography: Present and Future 6G 117
Dhananjay Manohar Dakhane, Vaibhav Eknath Narawade and Pallavi Sapkale
5.1 Introduction 117
5.2 Quantum Cryptography 119
5.3 Quantum Key Distribution 120
5.4 Post Quantum Cryptography 121
5.5 Conclusions 121
References 122
6 Network Intelligence with Quantum Computing for 6G 123
H. Bhoomeeswaran, G. Joshva Raj, J. Mangaiyarkkarasi and J. Shanthalakshmi Revathy
6.1 Introduction 124
6.2 Quantum Computing 127
6.3 Spintronic QC 127
6.4 Literature Survey 129
6.5 Shstno 130
6.6 Photonic QC 133
6.7 Conclusion 137
6.8 Future Scope 138
References 139
Part III: Machine Learning 141
7 Introduction to Machine Learning: Conceptualization, Implementation, and Research Perspective 143
Snehasis Dey
7.1 Introduction to Machine Learning: Conceptualization Perspective 144
7.1.1 Basics of Machine Learning 144
7.1.2 Literature Survey 147
7.1.3 Problem Statement and Proposed Model 147
7.1.4 Evolution of Machine Learning 148
7.1.5 Machine Learning as a Powerful Tool for Future Advancement 150
7.2 A Dive Into Machine Learning: Implementation Perspective 151
7.2.1 Correlations and Differences Between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) 151
7.2.2 Learning Techniques in Machine Learning 153
7.2.3 Algorithms in Machine Learning 155
7.3 Recent Trends in Machine Learning: Research Perspective 156
7.3.1 Machine Learning in Fourth Industry Revolution or Industry 4.0 (4IR) 157
7.3.2 Machine Learning in Real-World Applications: ml
for Everything, for Everywhere and for Everyone 157
7.3.3 Machine Learning in 5G Wireless Communications and Beyond 158
7.4 Conclusion 159
References 160
8 6G Wireless Networks: Pioneering with Machine Learning Technologies 161
Krupali Dhawale, Shraddha Jha, Mishri Gube, Shivraj Guduri and Khwaish Asati
8.1 Introduction 162
8.2 Introduction to 6G Wireless Networks and Machine Learning 162
8.2.1 6G Wireless Network and Its Significance 162
8.2.2 Challenges for 6G Networks 164
8.2.3 Goals for 6G Networks 166
8.2.4 Introduction to Machine Learning and Its Relevance in Wireless Networks 167
8.2.4.1 Importance of Machine Learning in Wireless Networks 168
8.2.5 Potential Benefits of Integrating Machine Learning in 6G Technology 169
8.3 Machine Learning Techniques for 6G Wireless Networks 171
8.3.1 Signal Processing and Optimization 171
8.3.2 Adaptive Beamforming and Spatial Processing Using Machine Learning 173
8.3.3 Benefits of Adaptive Rendering and Spatial Processing through Machine Learning 174
8.3.4 Signal Denoising, Interference Mitigation, and Resource Allocation 174
8.3.5 Spectrum Management and Allocation 176
8.4 Driven Network Management and Security 178
8.4.1 Self-Organizing Networks (SON) 178
8.4.2 Automatic Network Configuration and Optimization through AI 180
8.4.2.1 Network Management 180
8.4.2.2 Network Security 181
8.4.3 Fault Detection, Self-Healing, and Network Maintenance 181
8.5 Challenges and Future Directions 182
8.5.1 Data Privacy and Ethics Issues and Challenges 182
8.5.2 Future Directions in Data Privacy and Ethical Considerations 183
8.5.3 Balancing Data Usage and User Privacy in AI-Driven Networks 184
8.6 Conclusion 185
References 186
9 Machine Learning-Based Communication and Network Automation: Advancements, Challenges, and Prospects 187
J. Shanthalakshmi Revathy and J. Mangaiyarkkarasi
9.1 Introduction 188
9.2 Advancements in Machine Learning for Communication and Network Automation 189
9.2.1 Machine Learning Fundamentals 190
9.2.1.1 Supervised, Unsupervised, and Reinforcement Learning in Network Automation 190
9.2.2 Applications in Network Automation 192
9.2.2.1 Predictive Maintenance and Fault Detection 192
9.2.2.2 Quality of Service (QoS) Optimization 193
9.2.2.3 Traffic Engineering and Load Balancing 193
9.2.3 Data Sources and Preprocessing 194
9.2.3.1 Data Collection Methods in Network Environments 194
9.2.3.2 Data Preprocessing Techniques 195
9.2.3.3 Feature Selection and Engineering 195
9.2.4 Model Training and Deployment 196
9.3 Challenges in Implementing Machine Learning for Network Automation 199
9.3.1 Data Quality and Availability 199
9.3.2 Scalability and Resource Constraints 200
9.3.3 Interoperability and Standards 201
9.3.3.1 Need for Standardization 201
9.3.3.2 Compatibility with Existing Network Infrastructure 202
9.3.3.3 Vendor-Specific Challenges 202
9.3.4 Ethical and Regulatory Considerations 203
9.3.4.1 Bias and Fairness in Machine Learning Algorithms 203
9.3.4.2 Regulatory Compliance in Network Automation 204
9.3.4.3 Ethical Implications of Automation in Communication 205
9.4 Prospects and Future Directions 206
9.4.1 Emerging Technologies 206
9.4.2 AI-Driven Autonomous Networks 208
9.4.2.1 Toward Fully Autonomous Networks 208
9.4.2.2 Self-Healing and Self-Optimizing Networks 208
9.4.2.3 Human-Machine Collaboration in Network Management 208
9.5 Research and Development Trends 209
9.5.1 Current Research Trends in Machine Learning and Network Automation 209
9.5.2 Industry Collaborations and Academic Contributions 210
9.5.3 The Importance of Open-Source Projects 211
9.6 Conclusion 212
References 213
10 Empowering 6G Communication Systems: Harnessing Machine Learning for Advancements in Flexible and 3D-Printed Antennas 217
Duygu Nazan Gençölan and Shilpa Mehta
10.1 Introduction 218
10.2 Flexible and 3D-Printed Antennas 222
10.3 Challenges in 6G Antenna Design 224
10.4 Machine Learning for Antenna Design 225
10.5 Data-Driven Antenna Optimization 226
10.6 Topology Optimization with ml 227
10.7 Material Selection and Optimization 229
10.8 Simulation and Modeling with ml 230
10.9 Hardware-Software Co-Design for ML-Aided Antennas 231
10.10 Experimental Validation and Prototyping 232
10.11 Conclusion and Future Directions 232
10.12 Future Directions 233
References 233
11 Potential Communication in B5G Networks Through Hybrid Millimeter-Wave Beamforming and Machine Learning: Basics, Challenges, and Future Path 243
Snehasis Dey
11.1 Introduction 244
11.2 Literature Survey 245
11.3 HBF Open Challenges 251
11.4 Conclusion 258
Bibliography 258
12 Device-to-Device Communication in 6G Using Machine Learning 261
J. Shanthalakshmi Revathy, J. Mangaiyarkkarasi and J. Matcha Rani
12.1 Introduction 262
12.2 Fundamentals of Device-to-Device Communication 263
12.3 Evolution from Previous Generations 265
12.3.1 Early Foundations: Peer-to-Peer and Ad Hoc Networks 265
12.3.2 Device-to-Device Communications in Cellular Networks 266
12.3.3 The 5G Era 267
12.3.4 Enhancement in 6G 267
12.4 Role of Machine Learning in 6G D2D Communication 268
12.4.1 Supervised Learning 268
12.4.2 Unsupervised Learning 269
12.4.3 Reinforcement Learning 270
12.4.4 Integration of Machine Learning in 6G Networks 271
12.5 Applications of Machine Learning in D2D Communication Resource Allocation and Spectrum Management 273
12.6 Challenges and Solutions 275
12.7 Case Studies 277
12.7.1 Smart Cities and Urban IoT Networks 277
12.7.2 Autonomous Vehicles and Vehicular Communication 278
12.7.3 Healthcare and Wearable Devices 278
12.7.4 Augmented Reality (AR) and Immersive Media 279
12.8 Challenges and Future Scope 279
12.9 Conclusion 280
References 281
Part IV: Quantum Computing and Machine Learning 283
13 Integrating Quantum Computing and Machine Learning in 6G Networks 285
Ogobuchi D. Okey, Theodore T. Chiagunye, Henrietta U. Udeani, Ikechukwu Nicholas, Renata L. Rosa and Demóstenes R. Zegarra
13.1 Introduction 286
13.2 Background Study 288
13.2.1 Technology Evolutionary Trends Toward 6G Network 288
13.2.2 Unique Features of 6G Networks 289
13.2.3 The Principle of Quantum Computing 291
13.2.4 Machine Learning 292
13.3 Quantum Machine Learning Algorithms and Implementation Frameworks 294
13.4 Resource Allocation in QML-Enabled 6G Network 300
13.5 Security Challenges and Prospects in QML 6G 301
13.6 Limitations, Benefits, and Future Directions 303
13.7 Conclusion 305
References 305
14 A Quantum Computing Perspective in 6G Networks: The Challenge of Adaptive Network Intelligence 311
Pallavi Sapkale
14.1 Introduction 312
14.1.1 Quantum Computing in 6G 312
14.2 What is Network Intelligence in Quantum Computing? 313
14.2.1 Methods to Achieve the Network Intelligence in Quantum Computing 317
14.3 How to Accomplish Network Intelligence 319
14.4 Quantum Computing Opportunities with 6G 319
14.5 Challenges and Research Scope in Quantum Computing with 6G 320
14.5.1 Main Challenges in Quantum Computing with 6G 320
14.5.2 Research Scope in Quantum Computing 322
14.6 Conclusion 323
References 324
15 Role of QML in 6G Integrated Vehicular Networks 327
R. Palanivel, Muthulakshmi P., Snehasis Dey, Shilpa Mehta and Pallavi Sapkale
15.1 Introduction 328
15.2 Literature Survey 331
15.3 Methodology 332
15.3.1 Quantum Machine Learning for Traffic Prediction in 6G Networks 332
15.3.1.1 Environment Setup 333
15.3.1.2 Quantum Traffic Prediction Model 333
15.3.1.3 Quantum Circuit Representation 333
15.3.1.4 Safety Analysis 334
15.3.1.5 Interpretation 334
15.3.2 QML for Network Security in Vehicular Communication 336
15.3.2.1 Environment Setup 336
15.3.2.2 Quantum Key Distribution (QKD) 337
15.3.2.3 Node1's Side (Initialization) 337
15.3.2.4 Quantum Communication Channel (Simulated) 337
15.3.2.5 Node2's Measurement 338
15.3.2.6 Security and Key Sharing 338
15.3.2.7 Interpretation 338
15.3.3 6G Network Slicing and Resource Management by QML 339
15.3.3.1 Environment Setup 339
15.3.3.2 Implementation Process 340
15.3.3.3 Interpretation 341
15.3.4 Quality-of-Service (QoS) Optimization by QML 342
15.3.4.1 Environment Setup and Parameters 342
15.3.4.2 Implementation Process 342
15.3.4.3 Interpretation 343
15.4 Results and Discussion 344
15.5 Conclusion 346
References 346
Part V: Applications 349
16 Smart Irrigation Technique Using IoT Based on 5G 351
Jyoti B. Deone and Khan Rahat Afreen
16.1 Introduction 352
16.2 Related Work 353
16.3 5G Network on Smart Farming 357
16.3.1 The Following Decade Will See the Development of 5G and Smart Farming 358
16.4 Proposed Methodology 359
16.5 Working Modules of the System 360
16.5.1 Login and Registration Module 360
16.5.2 Change Number Module 360
16.5.3 Check Status Module 360
16.5.4 Start Water Pump Module 360
16.5.5 Stop Water Pump Module 361
16.5.6 Force Start Water Pump Module 361
16.5.7 Auto Stopped Module 361
16.6 Experimental Result Analysis and Working 361
16.7 Conclusion 364
References 364
17 Modeling and Development of Low-Cost Visible Light Communication System 367
Mrinmoyee Mukherjee, Kevin Noronha and Ravi Kumar Bandi
17.1 Learning Objectives 368
17.2 Introduction to VLC 368
17.3 VLC System Description 374
17.3.1 Key Parameters - Light-Emitting Diode 375
17.3.2 Key Parameters - Photodiode 378
17.3.3 Key Parameters - VLC Channel 379
17.4 Experimental Implementation of the VLC System 382
17.4.1 Block Diagram and Technical Specifications 382
17.4.2 Results and Discussions 390
17.5 Simulation and Modeling of the VLC System 392
References 418
Index 423