Artificial Intelligence for 6G (2022) [Kim] [9783030950408]

Preface
Contents
Abbreviations
Part I Artificial Intelligence Techniques
1 Historical Sketch of Artificial Intelligence
	1.1 Introduction to Artificial Intelligence
	1.2 History of Artificial Intelligence
	References
2 Artificial Intelligence Ecosystem, Techniques, and Use Cases
	2.1 Artificial Intelligence Ecosystem
	2.2 Hardware and Software of Artificial Intelligence
	2.3 Artificial Intelligence Techniques and Selection
	2.4 Artificial Intelligence Workflow and Use Cases
	References
3 Unsupervised Learning
	3.1 Types and Performance Metrics of Unsupervised Learning
	3.2 Clustering Algorithms
		3.2.1 Hierarchical Clustering
		3.2.2 Partitional Clustering
	3.3 Association Rule Mining
	3.4 Dimensionality Reduction
	3.5 Problems
	References
4 Supervised Learning
	4.1 Supervised Learning Workflow, Metrics, and Ensemble Methods
	4.2 Classification of Supervised Learning
		4.2.1 Decision Tree
		4.2.2 K-Nearest Neighbours
		4.2.3 Support Vector Machine
	4.3 Regression of Supervised Learning
		4.3.1 Linear Regression
		4.3.2 Gradient Descent Algorithms
		4.3.3 Logistic Regression
	4.4 Problems
	References
5 Reinforcement Learning
	5.1 Introduction to Reinforcement Learning and Markov Decision Process
	5.2 Model-Based Approaches
		5.2.1 Policy Iteration
		5.2.2 Value Iteration
	5.3 Model-Free Approaches
		5.3.1 Monte Carlo Methods
		5.3.2 Temporal difference learning methods
	5.4 Problems
	References
6 Deep Learning
	6.1 Introduction to Deep Learning
	6.2 Deep Neural Network
	6.3 Convolutional Neural Network
	6.4 Recurrent Neural Network
	6.5 Problems
	References
Part II AI-Enabled Communications and Networks Techniques for 6G
7 6G Wireless Communications and Networks Systems
	7.1 6G Wireless Communications and Networks
		7.1.1 6G Use Cases and Requirements
		7.1.2 6G Timeline, Technical Requirements, and Technical Challenges
		7.1.3 6G Key Enabling Techniques
	7.2 AI-Enabled 6G Wireless Communications and Networks
		7.2.1 AI and ML Contributions to Physical Layers
		7.2.2 AI and ML Contribution to Data Link and Network Layers and Open Research Challenges
	7.3 Problems
	References
8 AI-Enabled Physical Layer
	8.1 Design Approaches of AI-Enabled Physical Layer
	8.2 End-To-End Physical Layer Redesign with Autoencoder
	8.3 Wireless Channel Models
	8.4 Signal Detection and Modulation
	8.5 Channel Estimation
	8.6 Error Control Coding
	8.7 MIMO
	8.8 Problems
	References
9 AI-Enabled Data Link Layer
	9.1 Design Approaches of AI-Enabled Data Link Layer
	9.2 Radio Resource Allocation and Scheduling
		9.2.1 Resource Allocation Problems in Wireless Networks and Convex Optimization
		9.2.2 Resource Allocation Models and Performance Measure
		9.2.3 Utility Functions and Fairness of Resource Allocation
		9.2.4 Resource Allocation Using AI Techniques
	9.3 Handover Using AI Techniques
	9.4 Problems
	References
10 AI-Enabled Network Layer
	10.1 Design Approaches of AI-Enabled Network Layer
	10.2 Cellular Systems and Networking
		10.2.1 Evolution of Cellular Networks
		10.2.2 Concept of Cellular Systems
		10.2.3 Cell Planning
	10.3 Network Traffic Prediction
		10.3.1 Classic Network Traffic Prediction
		10.3.2 AI-Enabled Network Traffic Prediction
	10.4 Problems
	References
Index