Название: Networked Artificial Intelligence: AI-Enabled 5G NetworkingАвтор: Radhika Ranjan Roy
Издательство: CRC Press
Год: 2025
Страниц: 220
Язык: английский
Формат: pdf (true)
Размер: 24.5 MB
The integration of fifth generation (5G) wireless technologies with distributed Artificial Intelligence (AI) is transforming network operations. AI is increasingly embedded in all network elements, from cloud and edge to terminal devices, enabling AI to function as a networking system. This convergence facilitates AI-based applications across the global network, with notable successes in various domains such as computer vision, natural language processing, and healthcare. Networked Artificial Intelligence: AI-Enabled 5G Networking a comprehensive framework for the deep integration of computing and communications, optimizing networks and applications as a unified system using AI.
The book covers topics ranging from networked AI fundamentals to AI-enabled 5G networks, including agent modeling, machine learning (ML) algorithms, and network protocol architectures. It discusses how network service providers can leverage AI and ML techniques to customize network baselines, reduce noise, and accurately identify issues. It also looks at AI-driven networks that enable self-correction for maximum uptime and prescriptive actions for issue resolution, as well as troubleshooting by capturing and storing data before network events.
The book presents a comprehensive approach to AI-enabled networking that offers unprecedented opportunities for efficiency, reliability, and innovation in telecommunications. It works through the approach’s five steps of connection, communication, collaboration, curation, and community. These steps enhance network effects, empowering operators with insights for trusted automation, cost reduction, and optimal user experiences. The book also discusses AI and ML capabilities that enable networks to continuously learn, self-optimize, and predict and rectify service degradations proactively, even with full automation.
Network service providers can customize the network baseline for alerts, reducing noise, and false positives while enabling information technology (IT) teams to accurately identify issues, trends, anomalies, and root causes. AI/ML/DL techniques are also used to reduce unknowns and improve the level of certainty in decision making. Networked AI/ML/DL even can enable IT systems to self-correct for maximum uptime and provide prescriptive actions as to how to fix problems that occur. In addition, AI-driven networks can capture and save data prior to a network event or outage, helping to speed up troubleshooting. Connection, communication, collaboration, curation, and community are the five steps that help to boost network effects. Network operators gain insights through analytics and AI/ML/DL that guide more trusted automation processes that lower the cost of network operations and provide users with an optimal connected experience. One important thing is that AI/ML/DL will increasingly enable networks to continually learn, self-optimize, and even predict and rectify service degradations before they occur even with full automation.
This book has 18 chapters:
1. Networked Artificial Intelligence
2. Artificial Intelligent Agent
3. Agent Function
4. Agent Modeling
5. Multi-Agent System
6. Protocol Layer Architecture
7. Artificial Intelligence Performance Analysis
8. Unsupervised Machine Learning
9. Supervised Machine Learning
10. Deep Learning
11. Overfitting and Underfitting
12. Hybrid Learning
13. Reinforcement Learning
14. Artificial Intelligence Application and Network Protocol Architecture Model
15. AI-enabled Network
16. AI-enabled End-to-End Network
17. AI-enabled Peer-to-Peer Network
18. Artificial Intelligence-Enabled 5G Network
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