Machine Learning and Wireless Communications (eBook, PDF)

Redaktion: Eldar, Yonina C.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 30. Juni 2022
• Englisch
• ISBN-13: 9781108967730
• Artikelnr.: 66182019

Autorenporträt

Weizmann Institute of Science, Israel

Inhaltsangabe

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.