Quantum Computing and Machine Learning for 6g

1. Auflage 2026
ISBN: 978-1-394-23808-8
Verlag: Wiley

Buch, Englisch, 464 Seiten

Autoren/Hrsg.

Sapkale, Pallavi

Mehta, Shilpa

Balamurugan, S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Technische Informatik Quantencomputer, DNA-Computing

Inhaltsverzeichnis

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
1.2 Generations of Wireless Communication 8
1.3 1G to 4G: Evolution of Wireless Standards 15
1.4 Industry and Research Initiatives for 6G 22

2 The State-of-the-Art and Future Visioning 6G Wireless Network 29
Payal Bansal

2.1 Introduction 30
2.2 Handover Management in 6G 34
2.3 Two-Tier Network Handover Skipping 43

Part II: Quantum Computing 55

3 Introduction to Quantum Computing 57
Shilpa Mehta and Celestine Iwendi

3.1 Introduction 57
3.2 Quantum Gates 62
3.3 Quantum Algorithms 64
3.4 Quantum Hardware and Software 72
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

4 Quantum-Secured Concealed Identifier for 6G Technology 89
Pratham Desai and Dipali Kasat

4.1 Quantum Mechanical Properties for Security 90
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
4.7 A Post-Quantum SUCI for 6G 105
4.8 Comparison Between the Existing Schemes 111

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

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

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
7.2 A Dive Into Machine Learning: Implementation Perspective 151 Contents xi
7.3 Recent Trends in Machine Learning: Research Perspective 156
7.4 Conclusion 159

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
8.3 Machine Learning Techniques for 6G Wireless Networks 171
8.4 Driven Network Management and Security 178
8.5 Challenges and Future Directions 182
8.6 Conclusion 185

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
9.3 Challenges in Implementing Machine Learning for Network Automation 199
9.4 Prospects and Future Directions 206
9.5 Research and Development Trends 209
9.6 Conclusion 212

10 Empowering 6G Communication Systems: Harnessing Machine Learning for Advancements in Flexible and 3D-Printed Antennas 217
Duygu Nazan Gençoðlan 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

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

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
12.4 Role of Machine Learning in 6G D2D Communication 268
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
12.8 Challenges and Future Scope 279
12.9 Conclusion 280

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
13.3 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

14 A Quantum Computing Perspective in 6G Networks: The Challenge of Adaptive Network Intelligence 311
Pallavi Sapkale

14.1 Introduction 312
14.2 What is Network Intelligence in Quantum Computing? 313
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
14.6 Conclusion 323

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
15.4 Results and Discussion 344
15.5 Conclusion 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
16.4 Proposed Methodology 359
16.5 Working Modules of the System 360
16.6 Experimental Result Analysis and Working 361
16.7 Conclusion 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
17.4 Experimental Implementation of the VLC System 382
17.5 Simulation and Modeling of the VLC System 392

References 418
Index 423

Über Autor(innen)

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.