5G technologies are enabling AI-based applications over the global network. This book provides a unified framework for the deep convergence of computing and communications, where the network and application/service can be jointly optimized as a single integrated system using AI. It presents a full treatment of 5G networked AI.
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.
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
Radhika Ranjan Roy is an electronics engineer, US Army Research, Development, and Engineering Command (RDECOM), CommunicationsElectronics Research, Development, and Engineering Center (CERDEC), Space and Terrestrial Communications Directorate (S&TCD) Laboratories, Aberdeen Proving Ground (APG), Maryland, since 2009. Before joining to US Army Research, he worked in various capacities in CACI, SAIC, AT&T/Bell Laboratories, CSC, and PDB since his graduation. He earned his PhD in electrical engineering with major in computer communications from the City University of New York, New York, in 1984, and MS in electrical engineering from the Northeastern University, Boston, Massachusetts, in 1978. He received his BS in electrical engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in 1967. He has published more than 50 technical papers. He is holding and/or submitted over 30 patents. He authored a book titled Handbook of Mobile Ad Hoc Networks on Mobility Models in 2010.
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