Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
SG Glisic
Résumé
A practical overview of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks
Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency.
In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks , the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few.
The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from:
- A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines
- An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more
- Discussions of explainable neural networks and XAI
- Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology
Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.
Preface
PART 1:ARTIFICIAL INTELLIGENCE
Ch 1. INTRODUCTION
1.1 Motivation
1.2 Book Structure
Ch 2 ML ALGORITHMS
2.1. Fundamentals
2.1.1. Linear Regression
2.1.2 Logistic Regression
2.1.3 Decision Tree: Regression Trees vs 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. SVM Support Vector Machine
2.1.8 Naive Bayes , kNN, K Means
2.1.9 Dimensionality Reduction
2.2. ML Algorithms Analysis
2.2.1 Logistic Regression
2.2.2. Decision Trees Classifiers
2.2.3 Dimensionality reduction techniques
2 REFERENCES
Ch 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 CoNN
3.6.1 CoNN Architecture
3.6.2 Layers in CoNN
3 REFERENCES
Ch 4 EXPLAINABLE NN
4.1 Explainability Methods
4.1.1 The complexity and Interoperability
4.1.2 Global Versus Local Interpretabity
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 Support Vector Regression
4.4.3 Combination of Fuzzy Models and SVR
4 REFERENCES
Ch 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 Recurrent Graph Neural Networks (RecGNNs)
5.2.2 Convolutional Graph Neural Networks (ConvGNNs)
5.2.3 Graph Autoencoders (GAEs)
5.2.4 Spatial-Temporal Graph Neural Networks (STGNNs)
5.3 Complexity of NN
5.3.1 Labeled Graph NN (LGNN)
5.3.2 Computational Complexity
Appendix 5.1
Appendix 5.2 Graph Fourier Transform
Ch 6 LEARNING EQUILIBRIA AND GAMES
6.1 Learning in Games
6.1.1 Learning Equilibria of Games
6.2 Online Learning of Nash Equilibria
in Congestion Games
6.3 Minority Games
6.4 Nash Q-Learning
6.4.1 Multiagent 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 Price of Anarchy
6.6. Routing with Edge Priorities
6.6.1 Computing Equilibria
6 REFERENCES
Ch 7 AI ALGORITHMS IN NETWORKS
7.1. AI Based Algorithms in Networks
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 Non-Cooperative 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
7.4.2a SON Function Model
7.4.2b 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 Networks Optimization
7.7. Graph Neural Networks for
7.7.1 Network Virtualization
7.7.2 GNN-Based Dynamic Resource Management
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 Multiarmed Bandit Estimator MBE
7.10.1 System Model
7.10.2 System Performance
7.11 Network Representation Learning
7.11.1Network properties
7.11.2 Unsupervised NRL
7.11.3 Semi-Supervised NRL
7 REFERENCES
PART 2:QUANTUM COMPUTING
Ch8 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
8.3.1 QFT vs FFT Revisited
8 REFERENCE
Ch 9 QUANTUM CHANNEL
INFORMATION THEORY
9.1 Communication Over a Q Channel
9.1 Quantum Information Theory
9.1.1 Density Matrix and Trace Operator
9.1.2 Quantum Measurement
9.2 Q Channel Description
9.2.1 Q Channel Entropy
9.2.2 A Bit on History
9.3 Q Channel Classical Capacities
9.3.1 Capacity of Classical Channels
9.3.2 The Private Classical Capacity
9.3.3 The Entanglement-Assisted Classical Capacity
9.3.4 The Classical Zero-Error Capacity
9.3.5 Entanglement-Assisted Classical Zero-Error Capacity
9.4 Q Channel Quantum Capacity
9.4.1 Preserving Quantum Information
9.4.2 Quantum Coherent Information
9.4.3 Connection Between Classical and Quantum Information
9.5 Quantum Channel Examples
9.5.1. Channel Maps
9.5.2. Capacities
9.5.3 Q Channel Parameters
9 REFERENCES
Ch 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 error correction
10.4.2. Theory of Quantum Error Correction
Appendix: Binary fields and discrete vector spaces
Appendix 1: A Bit on Noise Physics
10 REFERENCES
Ch 11 QSA 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-Hoyer-Tapp Quantum Search Algorithm
11.1.8 Durr-Hoyer Quantum Search Algorithm
11.1.9 Quantum Counting Algorithm
11.1.10 Quantum Heuristic Algorithm
11.1.11 Quantum Genetic Algorithm
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 and Jozsa's Algorithm
11.2.3 Ethan Bernstein and Umesh Vazirani Implementation
11.2.4 Implementation of Quantum Fourier Transform
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
11.2.9 DESIGN EXAMPLE2): Grover's algorithm
11.2.10 DESIGN EXAMPLE3) :Simon's algorithm
11.2.11 DESIGN EXAMPLE4) : Shor's Algorithm
11 REFERENCES
Ch12. QUANTUM MACHINE LEARNING
12.1 Quantum machine learning algorithms
12.2 Quantum Neural Network 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 Quantum Neural Networks
12.5.1 Representation
12.5.2 Learning
12.6 Quantum Decision Tree Classifier
12.6.1 Model of the Classifier
APPENDIX: Matrix Exponential.
12 REFERENCES
Ch 13 QC OPTIMIZATION
13.1 Optimization for hybrid quantum -classical algorithms
13.1.1 Quantum Approximate Optimization Algorithm (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 DESIGN EXAMPLE: QC for Multiple Regression
13.5.1 Quantum Circuit
13.6 Quantum Algorithm for Systems of Nonlinear Differential Equations
13 REFERENCES
Ch 14 QUANTUM DECISION THEORY
14.1 Potential Enablers for Qc
14.2 Quantum Game Theory
14.2.1 Definitions
14.2.2 Quantum Games
14.2.3. DESIGN EXAMPLE: Quantum routing games
14.2.4 Quantum Game for Spectrum Sharing
14.3 Quantum Decision Theory (QDT)
14.3.1 Model: quantum decision theory
14.4 Predictions in Quantum Decision Theory
14.4.1 Utility Factors
14.4.2 Classification of Lotteries by Attraction Indices
14 REFERENCES
Ch 15 QC IN WIRELESS NETWORKS
15.1 Quantum Satellite Networks
15.1.1 Satellite-Based QKD System
15.1.2 Quantum Satellite Networks 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 Quantum Key Distribution Networks
15.3.1 QOS in QKD Overlay Networks
15.3.2 Adaptive QoS-QKD Networks
15.3.3 Routing Protocol for QKD Network
15 REFERENCE:
Ch 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 Quantum walks
16.3.1 Discrete quantum walks on a line (DQWL)
16.3.2 Performance study of DQWL
16.4 Multidimensional Quantum Walks
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
16 REFERENCES
Ch 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 QN on Recursively Generated Graphs (RGG)
17.1.5 Recursively Generated Virtual Graph
17.2 QN Protocol Stack
17.2.1 Preliminaries
17.2.2 QN Protocol Stack
17.2.3 Layer 3- Reliable State Linking
17.2.4 Layer 4- Region Routing
17 REFERENCES
Index
Savo G. Glisic is Research Professor at Worcester Polytechnic Institute, Massachusetts, USA. His research interests include network optimization theory, network topology control and graph theory, cognitive networks, game theory, artificial intelligence, and quantum computing technology.
Beatriz Lorenzo is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, USA. Her research interests include the areas of communication networks, wireless networks, and mobile computing.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Wiley |
| Auteur(s) | SG Glisic |
| Parution | 16/03/2022 |
| Nb. de pages | 864 |
| EAN13 | 9781119790297 |
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