AI-Enabled 6G Networks and Applications

AI-ENABLED 6G NETWORKS AND APPLICATIONS

Provides authoritative guidance on utilizing AI techniques in 6G network design and optimization

Written and edited by active researchers, this book covers hypotheses and practical considerations and provides insights into the design of evolutionary AI algorithms for 6G networks, with focus on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields.

AI-Enabled 6G Networks and Applications includes a 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 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.

• Reviews various types of AI techniques such as AI-empowered mobile edge computing, intelligent handover management, and smart spectrum management
• Describes how AI techniques manage computation efficiency, algorithm robustness, hardware development, and energy management
• Identifies and provides solutions to problems in current 4G/5G networks and emergent 6G architectures
• Discusses privacy and security issues in IoT-enabled 6G Networks
• Examines the use of machine learning to achieve closed-loop optimization and intelligent wireless communication

AI-Enabled 6G Networks and Applications is an essential reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols, suitable for graduate students and researchers in network forensics and optimization, computer science, and engineering.

AI-ENABLED 6G NETWORKS AND APPLICATIONS

提供在6G網路設計和優化中運用AI技術的權威指南

這本書由活躍的研究人員撰寫和編輯，涵蓋了假設和實際考量，並提供了關於6G網路演化AI演算法設計的見解，重點放在網路透明度、可解釋性和模擬性方面，以應用於車載網路、太空系統、監視系統和不同新興工程領域。

AI-Enabled 6G Networks and Applications 包括對6G網路的AI技術進行回顧，並將重點放在部署AI技術以有效地優化網路性能上，包括AI增強的邊緣運算、智能移動性和切換管理，以及智能頻譜管理。本書還包括一組具有通信協議的演化AI混合演算法的設計，展示如何在實踐中使用它們來解決與車載網路、空中網路和通信網路相關的問題。

• 回顧了各種類型的AI技術，如AI增強的邊緣運算、智能切換管理和智能頻譜管理


• 描述了AI技術如何管理計算效率、演算法穩健性、硬體開發和能源管理


• 識別並提供了解決當前4G/5G網路和新興6G架構中的問題的解決方案


• 討論了物聯網啟用的6G網路中的隱私和安全問題


• 檢視了使用機器學習實現閉迴路優化和智能無線通信的方法

AI-Enabled 6G Networks and Applications 是一本關於6G支援網路和協議的先進混合計算智能方法的重要參考指南，適合網路取證和優化、計算機科學和工程領域的研究生和研究人員閱讀。

Deepak Gupta, Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. His research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.

Mahmoud Ragab, PhD., Department of Information Technology, King Abdulaziz University, Saudi Arabia. Head of the Biological Quantum Computing Unit in the Centre for Artificial Intelligence in Precision Medicine. Associate Professor, Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt.

Romany Fouad Mansour, PhD., Department of Mathematics, New Valley University, Egypt. His research interests include Artificial Intelligence, Pattern Recognition, Computer Vision, Computer Networks, Soft Computing, Image Processing, Bioinformatics and Evolutionary Computation.

Aditya Khamparia, Assistant Professor, Baba Saheb Bhimrao Amedkar (Central) University, Amethi, India. His teaching and research focus on machine learning, deep learning, educational technologies, and computer vision.

Ashish Khanna, Assistant Professor, Maharaja Agrasen Institute of Technology, Delhi, India. He has extensive academic and industry experience in areas such as image processing, distributed systems, and machine learning.

Deepak Gupta，印度德里Maharaja Agrasen Institute of Technology的計算機科學與工程系助理教授。他的研究興趣包括智能數據分析、自然啟發計算、機器學習和軟計算。

Mahmoud Ragab，沙特阿拉伯國王阿卜杜勒阿齊茲大學信息技術系。他是人工智能精準醫學中心生物量子計算單位的負責人。他還是埃及開羅阿爾阿茲哈爾大學數學系副教授。

Romany Fouad Mansour，埃及新谷大學數學系。他的研究興趣包括人工智能、模式識別、計算機視覺、計算機網絡、軟計算、圖像處理、生物信息學和進化計算。

Aditya Khamparia，印度阿梅蒂巴巴薩希布·布希姆勞中央大學助理教授。他的教學和研究重點是機器學習、深度學習、教育技術和計算機視覺。

Ashish Khanna，印度德里Maharaja Agrasen Institute of Technology的助理教授。他在圖像處理、分散系統和機器學習等領域擁有豐富的學術和行業經驗。