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.
Table of Contents:
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
