Machine Learning and Wireless Communications
Machine Learning and Wireless Communications
Andrea Goldsmith, Princeton University, New Jersey
Deniz Gündüz, Imperial College of Science, Technology and Medicine, London
H. Vincent Poor, Princeton University, New Jersey
$105.00
USDHow 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.
- Provides examples of interdisciplinary and multidisciplinary engineering technologies, showing the significant interactions of machine learning and wireless communications
- Introduces basic concepts and tools in machine learning, with numerous examples of applications, to give a comprehensive grounding in the subject
- Explains new communication techniques and protocols, together with the challenges of implementing machine learning applications, to provide new research directions
Product details
July 2022Adobe eBook Reader
9781108967730
0 pages
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- 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.
