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Machine Learning and Wireless Communications

$105.00 USD

Deniz Gündüz, Yonina Eldar, Andrea Goldsmith, H. Vincent Poor, David Burth Kurka, Milind Rao, Nariman Farsad, Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak, Muriel Medard, Hyeji Kim, Hengtao He, Hao Ye, Shi Jin, Geoffrey Y. Li, Nir Shlezinger, Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek, Alessio Zappone, Mérouane Debbah, Philippe Mary, Christophe Moy, Visa Koivunen, Feng Yin, Yue Xu, Shuguang Cui, Ziv Aharoni, Dor Zur, Ziv Goldfeld, Haim Permuter, Mehmet Emre Ozfatura, Shiqiang Wang, Tiffany Tuor, Kin K. Leung, Mingzhe Chen, Mohammad Mohammadi Amiri, Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim, Mehdi Bennis, Dongzhu Liu, Amir Sonee, Stefano Rini, Osvaldo Simeone, Sheng Zhou, Wenqi Shi, Xiufeng Huang, Zhisheng Niu
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  • Date Published: July 2022
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781108967730

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About the Authors
  • 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.

    • 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
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    Reviews & endorsements

    'Recommended.' J. Brzezinski, Choice

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    Product details

    • Date Published: July 2022
    • format: Adobe eBook Reader
    • isbn: 9781108967730
    • availability: 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.

  • Editors

    Yonina C. Eldar, Weizmann Institute of Science, Israel
    Yonina C. Eldar is a professor of Electrical Engineering at the Weizmann Institute of Science, where she heads the Center for Biomedical Engineering and Signal Processing. She is also a visiting professor at MIT and at the Broad Institute, and an adjunct professor at Duke University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE fellow, and a EURASIP fellow.

    Andrea Goldsmith, Princeton University, New Jersey
    Andrea Goldsmith is the Dean of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. She is a member of the US National Academy of Engineering and the American Academy of Arts and Sciences. In 2020, she received the Marconi Prize.

    Deniz Gündüz, Imperial College of Science, Technology and Medicine, London
    Deniz Gündüz is a professor of Information Processing in the Electrical and Electronic Engineering Department of Imperial College London in the UK, where he serves as the Deputy Head of the Intelligent Systems and Networks Group. He is also a part-time faculty member at the University of Modena and Reggio Emilia in Italy.

    H. Vincent Poor, Princeton University, New Jersey
    H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He is a member of the US National Academy of Engineering and the US National Academy of Sciences. In 2017, he received the IEEE Alexander Graham Bell Medal.

    Contributors

    Deniz Gündüz, Yonina Eldar, Andrea Goldsmith, H. Vincent Poor, David Burth Kurka, Milind Rao, Nariman Farsad, Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak, Muriel Medard, Hyeji Kim, Hengtao He, Hao Ye, Shi Jin, Geoffrey Y. Li, Nir Shlezinger, Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek, Alessio Zappone, Mérouane Debbah, Philippe Mary, Christophe Moy, Visa Koivunen, Feng Yin, Yue Xu, Shuguang Cui, Ziv Aharoni, Dor Zur, Ziv Goldfeld, Haim Permuter, Mehmet Emre Ozfatura, Shiqiang Wang, Tiffany Tuor, Kin K. Leung, Mingzhe Chen, Mohammad Mohammadi Amiri, Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim, Mehdi Bennis, Dongzhu Liu, Amir Sonee, Stefano Rini, Osvaldo Simeone, Sheng Zhou, Wenqi Shi, Xiufeng Huang, Zhisheng Niu

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