Networked Artificial Intelligence: AI-Enabled 5G Networking

Cover
Half Title
Title
Copyright
Dedication
Contents
Preface
Author
Chapter 1 Networked Artificial Intelligence
	1.1 Emergence of AI Technology
		1.1.1 Machine Learning
		1.1.2 Deep Learning and Neural Networks
		1.1.3 Generative Artificial Intelligence
	1.2 Usage of Artificial Intelligence
	1.3 Artificial Intelligence Enabling and Supporting Technologies
		1.3.1 Natural Language Processing
		1.3.2 Computer Vision
		1.3.3 Graphical Processing Units
		1.3.4 Internet-of-Things
		1.3.5 Advanced Algorithms
		1.3.6 Application Programming Interfaces
	1.4 Emergence of Extremely High-Speed Semiconductor Technologies
	1.5 Emergence of Extremely High-Bandwidth Networking Technologies
		1.5.1 Terrestrial Wireline Networks
		1.5.2 Fifth- and Sixth-Generation Wireless Networks
		1.5.3 High-Bandwidth Satellite Networks
	1.6 Open Systems Interconnection
	1.7 Internet Protocol Layer Model
	1.8 Summary
Chapter 2 Artificial Intelligent Agent
	2.1 Agent and Environment
	2.2 Agent System
		2.2.1 Controller
		2.2.2 Hierarchical Controller
		2.2.3 Body
		2.2.4 Environment
	2.3 Agent Architecture and Control
	2.4 Summary
Chapter 3 Agent Function
	3.1 Time
	3.2 Tracing Percept
	3.3 Tracing Command
	3.4 History
	3.5 Memory/Belief State
	3.6 Rationality
	3.7 Online and Offline Learning
	3.8 Action
		3.8.1 Solution Searching
		3.8.2 Reasoning
		3.8.3 Planning
		3.8.4 Action with Certainty
		3.8.5 Action with Uncertainty
		3.8.6 Action in Learning
	3.9 Summary
Chapter 4 Agent Modeling
	4.1 Agent
	4.2 Reasoning
		4.2.1 Cognitive Reasoning
		4.2.2 Reasoning with Constraints
		4.2.3 Reasoning with Uncertainty
		4.2.4 Knowledge Reasoning
	4.3 Learning
	4.4 Learning with Reasoning
	4.5 Federated Learning
	4.6 Ensemble Learning
	4.7 Machine Learning with Logical Reasoning
		4.7.1 Hybrid Learning
	4.8 Summary
Chapter 5 Multi-Agent System
	5.1 Overview
	5.2 Collaborative Prognostics
	5.3 Agent Typologies and Failure Modes
	5.4 Value Asset
	5.5 Digital Twin
	5.6 Mediator Agents
	5.7 Social Platform
	5.8 Multi-Agent Architecture
		5.8.1 Centralized
		5.8.2 Hierarchical
		5.8.3 Heterarchical
		5.8.4 Distributed
	5.9 Summary
Chapter 6 Protocol Layer Architecture
	6.1 Open Standard International Model
		6.1.1 Layer 1: Physical Layer
		6.1.2 Layer 2: Data Link Layer
		6.1.3 Layer 3: Network Layer
		6.1.4 Layer 4: Transport Layer
		6.1.5 Layer 5: Session Layer
		6.1.6 Layer 6: Presentation Layer
		6.1.7 Layer 7: Application Layer
	6.2 Internet Model
		6.2.1 Layer 1: Physical Layer
		6.2.2 Layer 2: Data Link Layer
		6.2.3 Layer 3: Internet/Network Layer
		6.2.4 Layer 4: Transport Layer
		6.2.5 Layer 5–6: Middleware
		6.2.6 Layer 7: Application Layer
	6.3 Peer-to-Peer Architecture Model
	6.4 Summary
Chapter 7 Artificial Intelligence Performance Analysis
	7.1 Signal/Data Acquisition
	7.2 Signal/Data Digitization
	7.3 Signal/Data Clearing and Preprocessing
	7.4 Feature Extraction
	7.5 Dimensionality Reduction
	7.6 Signal/Data Classification Techniques
	7.7 Signal/Data Classification Evaluation Criteria
		7.7.1 Confusion Matrix
		7.7.2 Macro- and Micro-Averaging
		7.7.3 Fβ-Score
		7.7.4 Matthews Correlation Coefficient (MCC)
		7.7.5 Receiver Operating Characteristics (ROC)
		7.7.6 Area under the ROC Curve (AUC)
		7.7.7 Examples
		7.7.8 Use of ROC Curve
	7.8 Summary
Chapter 8 Unsupervised Machine Learning
	8.1 Hierarchical Clustering
	8.2 Bayesian Clustering
	8.3 Partitional Clustering
		8.3.1 K-Means Clustering
		8.3.2 Mixture Models
	8.4 Applications of Clustering in Networks
	8.5 Latent Variable Model
		8.5.1 Mixture Distribution
		8.5.2 Factor Analysis
		8.5.3 Blind Signal Separation
		8.5.4 Non-Negative Matrix Factorization
		8.5.5 Hidden Markov Models
		8.5.6 Bayesian Networks and Probabilistic Graph Models
		8.5.7 Applications of Latent Variable Models in Networks
	8.6 Dimensionality Reduction
		8.6.1 ISOMAP
		8.6.2 Generative Topographic Mapping
		8.6.3 Locally Linear Embedding
		8.6.4 Principal Curve
		8.6.5 Nonlinear Multi-Dimensional Scaling
		8.6.6 T-Distributed Stochastic Neighbor Embedding
	8.7 Outlier Detection
		8.7.1 Nearest Neighbor Anomaly Detection
		8.7.2 Local Outlier Factor
		8.7.3 Connectivity-Based Outlier Factors
		8.7.4 Influenced Outlierness
		8.7.5 Local Outlier Probability Models
	8.8 Key Characteristic of Unsupervised Learning
	8.9 Applications of Unsupervised Learning in Networking
	8.10 Future Network: Research Challenges and Opportunities
		8.10.1 Simplified Network Management
		8.10.2 Semi-Supervised Learning for Computer Networks
		8.10.3 Transfer Learning in Computer Networks
		8.10.4 Federated Learning in Computer Networks
		8.10.5 Generative Adversarial Networks in Computer Networks
	8.11 Pitfalls and Caveats of Using ML/DL in Networking
		8.11.1 Inappropriate Technique Selection
		8.11.2 Lack of Interoperability of Some Supervised ML/DL Algorithms
		8.11.3 Lack of Operations Success of ML/DL Networking
		8.11.4 Ignoring Simple Non-ML/DL Based Tools
		8.11.5 Overfitting
		8.11.6 Data Quality Issues
		8.11.7 Inaccurate Model Building
		8.11.8 Machine Learning in Adversarial Environments
	8.12 Summary
Chapter 9 Supervised Machine Learning
	9.1 Linear Regression and Classification
	9.2 Logistic Regression
	9.3 Ridge Regression
	9.4 LASSO Regression
	9.5 Tree-Based Models
		9.5.1 Decision Tree
		9.5.2 Random Forests
		9.5.3 Gradient Boosting Regression
		9.5.4 XGBoost
		9.5.5 Light Gradient Boosted Machine Regressor
	9.6 Summary
Chapter 10 Deep Learning
	10.1 Cost and Error Function, Gradient Decent, and Backpropagation in Neural Network
		10.1.1 Gradient Descent in Neural Networks
		10.1.2 Two-Dimensional Gradient Descent Example and Backpropagation Depth
		10.1.3 Vectorization in Neural Networks
		10.1.4 Matrix Multiplication
		10.1.5 Cost/Error/Loss Function Derivatives for Gradient Descent
		10.1.6 Propagating into Hidden Layers
		10.1.7 Vectorization of Backpropagation
		10.1.8 Implementing Gradient Descent Step
		10.1.9 Final Gradient Descent Algorithm
	10.2 Summary
Chapter 11 Overfitting and Underfitting
	11.1 Overfitting
	11.2 Resolving Overfitting
		11.2.1 Linear Regression
		11.2.2 Ridge Regression
		11.2.3 Lasso Regression
		11.2.4 Early Stopping
		11.2.5 Cross-Validation
		11.2.6 Train with More Data
		11.2.7 Data Augmentation
		11.2.8 Feature Selection
	11.3 Underfitting
		11.3.1 Resolving Underfitting
	11.4 Summary
Chapter 12 Hybrid Learning
	12.1 Semi-Supervised Learning
	12.2 Self-Supervised Learning (SSL)
	12.3 Multi-Instance Learning
	12.4 Contrastive Self-Supervised Learning
	12.5 Non-Contrastive Self-Supervised Learning
	12.6 Self-Supervised Semi-Supervised Learning (S4L)
	12.7 Summary
Chapter 13 Reinforcement Learning
	13.1 RL Agent and Environment Interaction
		13.1.1 Policies
	13.2 Optimal Stationary Policy for Infinite Horizon Problems
	13.3 Value of a Policy
	13.4 Q-Table
	13.5 Value of an Optimal Policy
	13.6 Policy Categories
	13.7 Temporal Difference
		13.7.1 Temporal Differences (TD)
	13.8 Policy Gradient
	13.9 Expected Return
	13.10 V-Value Function
	13.11 Q-Value Function
	13.12 Fitted Q-Learning
	13.13 Deep Q-Networks
	13.14 Massive Parallel DQN Architecture
	13.15 DDQN: Double Deep Q-Networks
		13.15.1 Background
		13.15.2 Deep Q Networks (DQN)
		13.15.3 Double Deep Q-Networks (Double DQN)
	13.16 Neural Fitted Q-Network (NFQ)
	13.17 Advantage Actor-Critic Network
		13.17.1 Estimating Qπ
		13.17.2 Estimating Vπ
	13.18 Asynchronous Advantage Actor-Critic Network
		13.18.1 Asynchronous Advantage Actor-Critic
	13.19 Dueling Deep Q-Network
	13.20 Summary
Chapter 14 Artificial Intelligence Application and Network Protocol Architecture Model
	14.1 Artificial Intelligence-Aware Applications Services
	14.2 Artificial Intelligence-Enabled/Standard-Based Applications
	14.3 Artificial Intelligence-Enabled/Standard-Based Middleware Infrastructure
		14.3.1 Artificial Intelligence-Standard-Based Middleware
		14.3.2 Artificial Intelligence-Enabled Middleware
	14.4 Artificial Intelligence-Enabled/Standard-Based Transport Protocols
	14.5 Artificial Intelligence-Enabled/Standard-Based Network and Routing Protocols
	14.6 Artificial Intelligence-Enabled/Standard-Based Link/Medium Access Control (MAC) Protocols
	14.7 Artificial Intelligence-Enabled/Standard-Based Physical (PHY) Layer
	14.8 Summary
Chapter 15 AI-Enabled Network
	15.1 AI-Enabled Physical Layer
	15.2 AI-Enabled Link/Medium Access Control (MAC) Layer
	15.3 AI-Enabled Network Layer
	15.4 AI-Enabled Transport Network Layer
	15.5 AI-Enabled Middleware Layer
	15.6 AI-Enabled Session Layer
	15.7 AI-Enabled Presentation Layer
	15.8 AI-Enabled Application Layer
		15.8.1 AI/ML/DL-Enabled Cybersecurity
	15.9 Summary
Chapter 16 AI-Enabled End-to-End Network
	16.1 Overview
	16.2 Artificial Intelligence and Multidisciplinary Applications
	16.3 Common AI/ML/DL Communications Network Infrastructure
	16.4 AI/ML/DL Networking Architecture
	16.5 End-to-End AI-Enabled OSI Layer Using Common AI/ML/DL Infrastructure
	16.6 Networked AI-Enabled Applications
	16.7 Summary
Chapter 17 AI-Enabled Peer-to-Peer Network
	17.1 Summary
Chapter 18 Artificial Intelligence-Enabled 5G Network
	18.1 Overview
	18.2 5G Radio Access Network
	18.3 5G Radio Interface
	18.4 5G Core Network
	18.5 5G Protocol Stack
		18.5.1 5G Radio Network Nodes vs 5G Deployment Types
		18.5.2 5G User and Control Planes for eNB, gNB, and ng-eNB
		18.5.3 5G Control Plane: UE-to-AMF and UE-to-SMF Protocol Stack
		18.5.4 5G User Plane: UE-to-AMF and UE-to-SMF Protocol Stack
		18.5.5 5G Non-Standalone versus 5G Standalone Architecture
	18.6 5G Security
		18.6.1 5G Non-Standalone NR Security
		18.6.2 Evolution of the 5G Trust Model
		18.6.3 5G Phase 1 Security (Release 15)
	18.7 5G Network Slicing
	18.8 5G MAC Protocol
	18.9 5G Higher Layer Protocols
	18.10 AI-Enabled 5G Applications
	18.11 5G End-to-End Network Architecture with AI-Enabled Applications
	18.12 Summary
Index