Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

Cover
Title Page
Copyright Page
Contents
Preface
Part I Artificial Intelligence
	Chapter 1 Introduction
		1.1 Motivation
		1.2 Book Structure
		References
	Chapter 2 Machine Learning Algorithms
		2.1 Fundamentals
			2.1.1 Linear Regression
			2.1.2 Logistic Regression
			2.1.3 Decision Tree: Regression Trees Versus Classification Trees
			2.1.4 Trees in R and Python
			2.1.5 Bagging and Random Forest
			2.1.6 Boosting GBM and XGBoost
			2.1.7 Support Vector Machine
			2.1.8 Naive Bayes, kNN, k-Means
			2.1.9 Dimensionality Reduction
		2.2 ML Algorithm Analysis
			2.2.1 Logistic Regression
			2.2.2 Decision Tree Classifiers
			2.2.3 Dimensionality Reduction Techniques
		References
	Chapter 3 Artificial Neural Networks
		3.1 Multi-layer Feedforward Neural Networks
			3.1.1 Single Neurons
			3.1.2 Weights Optimization
		3.2 FIR Architecture
			3.2.1 Spatial Temporal Representations
			3.2.2 Neural Network Unfolding
			3.2.3 Adaptation
		3.3 Time Series Prediction
			3.3.1 Adaptation and Iterated Predictions
		3.4 Recurrent Neural Networks
			3.4.1 Filters as Predictors
			3.4.2 Feedback Options in Recurrent Neural Networks
			3.4.3 Advanced RNN Architectures
		3.5 Cellular Neural Networks (CeNN)
		3.6 Convolutional Neural Network (CoNN)
			3.6.1 CoNN Architecture
			3.6.2 Layers in CoNN
		References
	Chapter 4 Explainable Neural Networks
		4.1 Explainability Methods
			4.1.1 The Complexity and Interoperability
			4.1.2 Global Versus Local Interpretability
			4.1.3 Model Extraction
		4.2 Relevance Propagation in ANN
			4.2.1 Pixel-wise Decomposition
			4.2.2 Pixel-wise Decomposition for Multilayer NN
		4.3 Rule Extraction from LSTM Networks
		4.4 Accuracy and Interpretability
			4.4.1 Fuzzy Models
			4.4.2 SVR
			4.4.3 Combination of Fuzzy Models and SVR
		References
	Chapter 5 Graph Neural Networks
		5.1 Concept of Graph Neural Network (GNN)
			5.1.1 Classification of Graphs
			5.1.2 Propagation Types
			5.1.3 Graph Networks
		5.2 Categorization and Modeling of GNN
			5.2.1 RecGNNs
			5.2.2 ConvGNNs
			5.2.3 Graph Autoencoders (GAEs)
			5.2.4 STGNNs
		5.3 Complexity of NN
			5.3.1 Labeled Graph NN (LGNN)
			5.3.2 Computational Complexity
		Appendix 5.A Notes on Graph Laplacian
		Appendix 5.B Graph Fourier Transform
		References
	Chapter 6 Learning Equilibria and Games
		6.1 Learning in Games
			6.1.1 Learning Equilibria of Games
			6.1.2 Congestion Games
		6.2 Online Learning of Nash Equilibria in Congestion Games
		6.3 Minority Games
		6.4 Nash Q-Learning
			6.4.1 Multi-agent Q-Learning
			6.4.2 Convergence
		6.5 Routing Games
			6.5.1 Nonatomic Selfish Routing
			6.5.2 Atomic Selfish Routing
			6.5.3 Existence of Equilibrium
			6.5.4 Reducing the POA
		6.6 Routing with Edge Priorities
			6.6.1 Computing Equilibria
		References
	Chapter 7 AI Algorithms in Networks
		7.1 Review of AI-Based Algorithms in Networks
			7.1.1 Traffic Prediction
			7.1.2 Traffic Classification
			7.1.3 Traffic Routing
			7.1.4 Congestion Control
			7.1.5 Resource Management
			7.1.6 Fault Management
			7.1.7 QoS and QoE Management
			7.1.8 Network Security
		7.2 ML for Caching in Small Cell Networks
			7.2.1 System Model
		7.3 Q-Learning-Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks
			7.3.1 Stochastic Noncooperative Game
			7.3.2 Multi-Agent Q-Learning
			7.3.3 Q-Learning for Channel and Power Level Selection
		7.4 ML for Self-Organizing Cellular Networks
			7.4.1 Learning in Self-Configuration
			7.4.2 RL for SON Coordination
			7.4.3 SON Function Model
			7.4.4 Reinforcement Learning
		7.5 RL-Based Caching
			7.5.1 System Model
			7.5.2 Optimality Conditions
		7.6 Big Data Analytics in Wireless Networks
			7.6.1 Evolution of Analytics
			7.6.2 Data-Driven Network Optimization
		7.7 Graph Neural Networks
			7.7.1 Network Virtualization
			7.7.2 GNN-Based Dynamic Resource Management
			7.7.3 Learning and Adaptation
		7.8 DRL for Multioperator Network Slicing
			7.8.1 System Model
			7.8.2 System Optimization
			7.8.3 Game Equilibria by DRL
		7.9 Deep Q-Learning for Latency-Limited Network Virtualization
			7.9.1 System Model
			7.9.2 Learning and Prediction
			7.9.3 DRL for Dynamic VNF Migration
		7.10 Multi-Armed Bandit Estimator (MBE)
			7.10.1 System Model
			7.10.2 System Performance
		7.11 Network Representation Learning
			7.11.1 Network Properties
			7.11.2 Unsupervised NRL
			7.11.3 Semi-Supervised NRL
		References
Part II Quantum Computing
	Chapter 8 Fundamentals of Quantum Communications
		8.1 Introduction
		8.2 Quantum Gates and Quantum Computing
			8.2.1 Quantum Circuits
			8.2.2 Quantum Algorithms
		8.3 Quantum Fourier Transform (QFT)
			8.3.1 QFT Versus FFT Revisited
		References
	Chapter 9 Quantum Channel Information Theory
		9.1 Communication Over a Q Channel
		9.2 Quantum Information Theory
			9.2.1 Density Matrix and Trace Operator
			9.2.2 Quantum Measurement
		9.3 Q Channel Description
			9.3.1 Q Channel Entropy
			9.3.2 Some History
		9.4 Q Channel Classical Capacities
			9.4.1 Capacity of Classical Channels
			9.4.2 The Private Classical Capacity
			9.4.3 The Entanglement-Assisted Classical Capacity
			9.4.4 The Classical Zero-Error Capacity
			9.4.5 Entanglement-Assisted Classical Zero-Error Capacity
		9.5 Q Channel Quantum Capacity
			9.5.1 Preserving Quantum Information
			9.5.2 Quantum Coherent Information
			9.5.3 Connection Between Classical and Quantum Information
		9.6 Quantum Channel Examples
			9.6.1 Channel Maps
			9.6.2 Capacities
			9.6.3 Q Channel Parameters
		References
	Chapter 10 Quantum Error Correction
		10.1 Stabilizer Codes
		10.2 Surface Code
			10.2.1 The Rotated Lattice
		10.3 Fault-Tolerant Gates
			10.3.1 Fault Tolerance
		10.4 Theoretical Framework
			10.4.1 Classical EC
			10.4.2 Theory of QEC
		10.A Binary Fields and Discrete Vector Spaces
		10.B Some Noise Physics
		References
	Chapter 11 Quantum Search Algorithms
		11.1 Quantum Search Algorithms
			11.1.1 The Deutsch Algorithm
			11.1.2 The Deutsch–Jozsa Algorithm
			11.1.3 Simon´s Algorithm
			11.1.4 Shor´s Algorithm
			11.1.5 Quantum Phase Estimation Algorithm
			11.1.6 Grover´s Quantum Search Algorithm
			11.1.7 Boyer–Brassard–Høyer–Tapp QSA
			11.1.8 Dürr–Høyer QSA
			11.1.9 Quantum Counting Algorithm
			11.1.10 Quantum Heuristic Algorithm
			11.1.11 Quantum GA
			11.1.12 Harrow–Hassidim–Lloyd Algorithm
			11.1.13 Quantum Mean Algorithm
			11.1.14 Quantum Weighted Sum Algorithm
		11.2 Physics of Quantum Algorithms
			11.2.1 Implementation of Deutsch´s Algorithm
			11.2.2 Implementation of Deutsch–Jozsa Algorithm
			11.2.3 Bernstein and Vazirani´s Implementation
			11.2.4 Implementation of QFT
			11.2.5 Estimating Arbitrary Phases
			11.2.6 Improving Success Probability When Estimating Phases
			11.2.7 The Order-Finding Problem
			11.2.8 Concatenated Interference
		References
	Chapter 12 Quantum Machine Learning
		12.1 QML Algorithms
		12.2 QNN Preliminaries
		12.3 Quantum Classifiers with ML: Near-Term Solutions
			12.3.1 The Circuit-Centric Quantum Classifier
			12.3.2 Training
		12.4 Gradients of Parameterized Quantum Gates
		12.5 Classification with QNNs
			12.5.1 Representation
			12.5.2 Learning
		12.6 Quantum Decision Tree Classifier
			12.6.1 Model of the Classifier
		References
	Chapter 13 QC Optimization
		13.1 Hybrid Quantum-Classical Optimization Algorithms
			13.1.1 QAOA
		13.2 Convex Optimization in Quantum Information Theory
			13.2.1 Relative Entropy of Entanglement
		13.3 Quantum Algorithms for Combinatorial Optimization Problems
		13.4 QC for Linear Systems of Equations
			13.4.1 Algorithm in Brief
			13.4.2 Detailed Description of the Algorithm
			13.4.3 Error Analysis
		13.5 Quantum Circuit
		13.6 Quantum Algorithm for Systems of Nonlinear Differential Equations
		References
	Chapter 14 Quantum Decision Theory
		14.1 Potential Enablers for Qc
		14.2 Quantum Game Theory (QGT)
			14.2.1 Definitions
			14.2.2 Quantum Games
			14.2.3 Quantum Game for Spectrum Sharing
		14.3 Quantum Decision Theory (QDT)
			14.3.1 Model: QDT
		14.4 Predictions in QDT
			14.4.1 Utility Factors
			14.4.2 Classification of Lotteries by Attraction Indices
		References
	Chapter 15 Quantum Computing in Wireless Networks
		15.1 Quantum Satellite Networks
			15.1.1 Satellite-Based QKD System
			15.1.2 QSN Architecture
			15.1.3 Routing and Resource Allocation Algorithm
		15.2 QC Routing for Social Overlay Networks
			15.2.1 Social Overlay Network
			15.2.2 Multiple-Objective Optimization Model
		15.3 QKD Networks
			15.3.1 QoS in QKD Overlay Networks
			15.3.2 Adaptive QoS-QKD Networks
			15.3.3 Routing Protocol for QKD Network
		References
	Chapter 16 Quantum Network on Graph
		16.1 Optimal Routing in Quantum Networks
			16.1.1 Network Model
			16.1.2 Entanglement
			16.1.3 Optimal Quantum Routing
		16.2 Quantum Network on Symmetric Graph
		16.3 QWs
			16.3.1 DQWL
			16.3.2 Performance Study of DQWL
		16.4 Multidimensional QWs
			16.4.1 The Quantum Random Walk
			16.4.2 Quantum Random Walks on General Graphs
			16.4.3 Continuous-Time Quantum Random Walk
			16.4.4 Searching Large-Scale Graphs
		References
	Chapter 17 Quantum Internet
		17.1 System Model
			17.1.1 Routing Algorithms
			17.1.2 Quantum Network on General Virtual Graph
			17.1.3 Quantum Network on Ring and Grid Graph
			17.1.4 Quantum Network on Recursively Generated Graphs (RGGs)
			17.1.5 Recursively Generated Virtual Graph
		17.2 Quantum Network Protocol Stack
			17.2.1 Preliminaries
			17.2.2 Quantum Network Protocol Stack
			17.2.3 Layer 3 – Reliable State Linking
			17.2.4 Layer 4 – Region Routing
		References
Index
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