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Artificial Intelligent Techniques for Wireless Communication and Networking
Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 1
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Оглавление
Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Страница 7
Страница 8
Страница 9
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Страница 11
1
Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning
1.1 Introduction
1.2 Comprehensive Study 1.2.1 Introduction
1.2.2 Framework
1.2.3 Choice of the Learning Algorithm and Function Approximator Selection
1.2.3.1
Auxiliary Tasks
1.2.3.2
Modifying the Objective Function
i) Reward shaping
ii) Tuning the discount factor
1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 1.3.1 Value-Based Method
1.3.2 Policy-Based Method
1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 1.4.1 Applications
1.4.2 Challenges
Off-Line Learning
Learning From Limited Samples
High-Dimensional State and Action Spaces
Safety Constraints
Partial Observability
Reward Functions
Explainability/Interpretability
Real-Time Inference
Delayed Rewards
1.5 Conclusion
References
Страница 35
2
Impact of AI in 5G Wireless Technologies and Communication Systems
2.1 Introduction
2.2 Integrated Services of AI in 5G and 5G in AI
2.2.1 5G Services in AI
2.2.1.1
Next-Generation Edge Convergence With AI Systems on Chip
2.2.1.2
Massive Device Concurrency Replenishing AI Data Lakes in Real Time
2.2.1.3
Ultra-Fast, High-Volume Streaming for Low-Latency AI
2.2.2 AI Services in 5G 2.2.2.1
Distributed AI
2.2.2.2
AI for IT Operations (AIOps)
2.2.2.3
Network Slicing
2.2.3 Evolution With AI in the 5G Era 2.2.3.1
Agile Network Construction
2.2.3.2
Intelligent Operations and Management
2.2.3.3
Smart Operations
2.3 Artificial Intelligence and 5G in the Industrial Space
2.4 Future Research and Challenges of Artificial Intelligence in the Mobile Networks
2.4.1 Research Directions 2.4.1.1
AI Is Being Adopted Into Mobile Networks by Communication Service Provider Now
2.4.1.2
AI and Customer Experience
2.4.1.3
Recouping the Network Investments That 5G Demands
2.4.1.4
Data Challenges Presented by Artificial Intelligence Adoption
2.4.1.5
Network Intelligence and Automation
2.4.2 Challenges to a 5G-Powered AI Network
2.4.2.1
Dealing With Interference
2.4.2.2
Dealing With Latency
2.4.2.3
Solving Latency
2.5 Conclusion
References
Страница 62
3
Artificial Intelligence Revolution in Logistics and Supply Chain Management
3.1 Introduction
3.2 Theory—AI in Logistics and Supply Chain Market 3.2.1 AI Impacts
3.2.2 Revolutionizing Global Market
3.2.3 Role of AI
3.2.4 AI Trends in Logistics
3.2.4.1
Anticipatory Logistics
3.2.4.2
Self-Learning Systems
3.2.5 AI Trends in Supply Chain
3.3 Factors to Propel Business Into the Future Harnessing Automation 3.3.1 Logistics 3.3.1.1
Predictive Capabilities
3.3.1.2
Robotics
3.3.1.3
Big Data
3.3.1.4
Computer Vision
3.3.1.5
Autonomous Vehicles
3.3.2 Supply Chain 3.3.2.1
Bolstering Planning & Scheduling Activities
3.3.2.2
Intelligent Decision-Making
3.3.2.3
End-End Visibility
3.3.2.4
Actionable Analytical Insights
3.3.2.5
Inventory and Demand Management
3.3.2.6
Boosting Operational Efficiencies
3.4 Conclusion
References
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