AI-enabled 6G networks and applications / edited by Deepak Gupta, [and four others].

Format:

Book

Contributor:

Gupta, Deepak, Ph.D., editor.

Language:

English

Subjects (All):

Mobile communication systems.

Artificial intelligence.

Physical Description:

1 online resource (179 pages)

Place of Publication:

Hoboken, New Jersey : Wiley, [2023]

Summary:

"This book includes review of AI techniques for 6G Networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book also includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks and communication networks. It is intended as a reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols for graduate students and researchers in network forensics and optimization, computer science, and engineering. The chapters have been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of evolutionary AI algorithms for 6G networks with focuses on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields."-- Provided by publisher.

Contents:

Cover

Title Page

Copyright Page

Contents

List of Contributors

Preface

About the Editors

Chapter 1 Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks

1.1 Introduction

1.2 The Proposed Model

1.2.1 Network Model

1.2.2 Algorithmic Procedure of MFO Algorithm

1.2.3 Design of MMFO-EEC Technique

1.3 Experimental Validation

1.4 Conclusion

References

Chapter 2 A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks

2.1 Introduction

2.2 The Proposed Model

2.2.1 RNN Based Traffic Flow Forecasting

2.2.2 ASCE Based Data Offloading

2.2.3 SAE Based Cyberattack Detection

2.2.4 CSO Based Parameter Optimization

2.3 Performance Validation

2.4 Conclusion

Chapter 3 Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks

3.1 Introduction

3.2 The Proposed Model

3.2.1 Z-Score Normalization

3.2.2 DBN Based Prediction Model

3.2.3 HSGO Based Hyperparameter Optimization Model

3.3 Experimental Validation

3.4 Conclusion

Chapter 4 Crow Search Algorithm Based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment

4.1 Introduction

4.2 The Proposed Model

4.2.1 Overview of VQ

4.2.2 LBG Model

4.2.3 Process Involved in CSAVQ-ICIIoT Model

4.3 Results and Discussion

4.4 Conclusion

Chapter 5 Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks

5.1 Introduction

5.2 The Proposed Model

5.2.1 Process Involved in DOA

5.2.2 Steps Involved in Energy Management Scheme.

5.3 Experimental Validation

5.4 Conclusion

Chapter 6 Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks

6.1 Introduction

6.2 The Proposed Model

6.2.1 Image Augmentation and Annotation

6.2.2 RefineDet Based Object Detection

6.2.3 Hyperparameter Tuning Using AWO Algorithm

6.2.4 Distance Measurement

6.3 Results and Discussion

6.4 Conclusion

Chapter 7 Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks

7.1 Introduction

7.2 Related Works

7.3 The Proposed Model

7.4 Experimental Validation

7.5 Conclusion

Chapter 8 Elite Oppositional Hunger Games Search Optimization Based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks

8.1 Introduction

8.2 Related Works

8.3 The Proposed Model

8.3.1 Design of EOHGSO Algorithm

8.3.2 Application of EOHGSO Algorithm for CSS

8.4 Experimental Validation

8.5 Conclusion

Index

EULA.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Gupta, Deepak AI-Enabled 6G Networks and Applications

ISBN:

9781119812722

1119812720

9781119812708

1119812704

OCLC:

1341268723