| Titre : |
Artificial intelligence and quantum computing for advanced wireless networks |
| Type de document : |
texte imprimé |
| Auteurs : |
Savo G. Glisic, Auteur ; Beatriz Lorenzo, Auteur |
| Editeur : |
New York : John Wiley and Sons |
| Année de publication : |
2022 |
| Importance : |
XIII, 850 p. |
| Présentation : |
ill. |
| Format : |
27 cm |
| ISBN/ISSN/EAN : |
978-1-119-79029-7 |
| Note générale : |
Références bibliogr. en fin de chapitres. - Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Artificial intelligence
Quantum computing
Wireless communication systems
Intelligence artificielle
Informatique quantique
Transmission sans fil |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
"By increasing the density and number of different functionalities in wireless networks there is more and more need for the use of artificial intelligence for planning network deployment, running their optimization and dynamically controlling their operation. For example, machine learning algorithms are used for the prediction of traffic and network state in order to timely reserve resources for smooth communication with high reliability and low latency; Big data mining is used to predict customer behaviour and pre-distribute the information content across the network so that it can be efficiently delivered as soon as requested; Intelligent agents can search the internet on behalf of the customer in order to find the best options when it comes to buying any product online. This timely book presents a review of AI-based learning algorithms with a number of case studies supported by Python and R programs, providing a discussion of the learning algorithms used in decision making based on game theory and a number of specific applications in wireless networks, such as channel, network state and traffic prediction. It is expected that once quantum computing becomes a commercial reality, it will be used in wireless communications systems in order to speed up specific processes due to its inherent parallelization capabilities. This is a practical book packed with case studies and follows a basic through to advanced level path and is an ideal course accompaniment for graduate/masters students, and online professional study." |
| Note de contenu : |
Summary :
Part I Artificial Intelligence
1. Introduction
2. Machine Learning Algorithms
3. Artificial Neural Networks
4. Explainable Neural Networks
5. Graph Neural Networks
6. Learning Equilibria and Games
7. AI Algorithms in Networks
Part II Quantum Computing
8. Fundamentals of Quantum Communications
9. Quantum Channel Information Theory
10. Quantum Error Correction
11. Quantum Search Algorithms
12. Quantum Machine Learning
13. QC Optimization
14. Quantum Decision Theory
15. Quantum Computing in Wireless Networks
16. Quantum Network on Graph
17. Quantum Internet |
Artificial intelligence and quantum computing for advanced wireless networks [texte imprimé] / Savo G. Glisic, Auteur ; Beatriz Lorenzo, Auteur . - New York : John Wiley and Sons, 2022 . - XIII, 850 p. : ill. ; 27 cm. ISBN : 978-1-119-79029-7 Références bibliogr. en fin de chapitres. - Index Langues : Anglais ( eng)
| Mots-clés : |
Artificial intelligence
Quantum computing
Wireless communication systems
Intelligence artificielle
Informatique quantique
Transmission sans fil |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
"By increasing the density and number of different functionalities in wireless networks there is more and more need for the use of artificial intelligence for planning network deployment, running their optimization and dynamically controlling their operation. For example, machine learning algorithms are used for the prediction of traffic and network state in order to timely reserve resources for smooth communication with high reliability and low latency; Big data mining is used to predict customer behaviour and pre-distribute the information content across the network so that it can be efficiently delivered as soon as requested; Intelligent agents can search the internet on behalf of the customer in order to find the best options when it comes to buying any product online. This timely book presents a review of AI-based learning algorithms with a number of case studies supported by Python and R programs, providing a discussion of the learning algorithms used in decision making based on game theory and a number of specific applications in wireless networks, such as channel, network state and traffic prediction. It is expected that once quantum computing becomes a commercial reality, it will be used in wireless communications systems in order to speed up specific processes due to its inherent parallelization capabilities. This is a practical book packed with case studies and follows a basic through to advanced level path and is an ideal course accompaniment for graduate/masters students, and online professional study." |
| Note de contenu : |
Summary :
Part I Artificial Intelligence
1. Introduction
2. Machine Learning Algorithms
3. Artificial Neural Networks
4. Explainable Neural Networks
5. Graph Neural Networks
6. Learning Equilibria and Games
7. AI Algorithms in Networks
Part II Quantum Computing
8. Fundamentals of Quantum Communications
9. Quantum Channel Information Theory
10. Quantum Error Correction
11. Quantum Search Algorithms
12. Quantum Machine Learning
13. QC Optimization
14. Quantum Decision Theory
15. Quantum Computing in Wireless Networks
16. Quantum Network on Graph
17. Quantum Internet |
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