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Machine Learning and Wireless Communications by Yonina C. Eldar
US $113,95
CircaEUR 97,04
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- Compralo SubitoMachine Learning and Wireless Communications by Yonina C. Eldar
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Spedizione:
US $3,99 (circa EUR 3,40) USPS Media MailTM.
Oggetto che si trova a: Nashville, TN, Stati Uniti
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Consegna prevista tra il mar 29 lug e il lun 4 ago a 94104
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Numero oggetto eBay:394714598935
Specifiche dell'oggetto
- Condizione
- Publish Year
- 2022
- Book Title
- Machine Learning and Wireless Communications
- ISBN
- 9781108832984
Informazioni su questo prodotto
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1108832989
ISBN-13
9781108832984
eBay Product ID (ePID)
10057258914
Product Key Features
Number of Pages
575 Pages
Publication Name
Machine Learning and Wireless Communications
Language
English
Subject
Engineering (General), Signals & Signal Processing
Publication Year
2022
Features
New Edition
Type
Textbook
Subject Area
Technology & Engineering
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Length
9.9 in
Item Width
7 in
Additional Product Features
Intended Audience
College Audience
LCCN
2021-063108
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
621.382
Table Of Content
Preface; 1. Machine learning and communications: an introduction Deniz Gündüz, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Mérouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Gündüz and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Gündüz; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
Edition Description
New Edition
Synopsis
How can machine learning help the design of future communication networks - and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications - an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge., How can machine learning help the design of future communication networks? How can future wireless networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most impactful technologies of our age in this comprehensive book, with accessible introductions and real-world examples.
LC Classification Number
TK5103.2.M3156 2022
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