Machine learning and wireless communications:
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...
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| Other Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY
Cambridge University Press
2022
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| Subjects: | |
| Online Access: | DE-12 DE-634 DE-92 DE-91 DE-473 Volltext |
| Summary: | 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. |
| Item Description: | Title from publisher's bibliographic system (viewed on 20 Jun 2022) Deep neural networks for joint source-channel coding / David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz, Andrea Goldsmith -- Timely wireless edge inference / Sheng Zhou, Wenqi Shi, Xiufeng Huang, and Zhisheng Niu |
| Physical Description: | 1 Online-Ressource (xiv, 544 Seiten) |
| ISBN: | 9781108966559 |
| DOI: | 10.1017/9781108966559 |
Staff View
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| 520 | |a 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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| discipline | Informatik Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
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| doi_str_mv | 10.1017/9781108966559 |
| format | Electronic eBook |
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| isbn | 9781108966559 |
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| spelling | Machine learning and wireless communications edited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University Cambridge, United Kingdom ; New York, NY Cambridge University Press 2022 1 Online-Ressource (xiv, 544 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 20 Jun 2022) Deep neural networks for joint source-channel coding / David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz, Andrea Goldsmith -- Timely wireless edge inference / Sheng Zhou, Wenqi Shi, Xiufeng Huang, and Zhisheng Niu 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. Wireless communication systems Machine learning Mobilfunk (DE-588)4170280-3 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Mobile Telekommunikation (DE-588)4341131-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s Mobilfunk (DE-588)4170280-3 s Mobile Telekommunikation (DE-588)4341131-9 s DE-604 Eldar, Yonina C. 1973- (DE-588)1043849696 edt Goldsmith, Andrea 1964- (DE-588)173861636 edt Gündüz, Deniz 1976- (DE-588)1159788731 edt Poor, H. Vincent 1951- (DE-588)1069964891 edt Erscheint auch als Druck-Ausgabe 978-1-108-83298-4 https://doi.org/10.1017/9781108966559 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Machine learning and wireless communications Wireless communication systems Machine learning Mobilfunk (DE-588)4170280-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Mobile Telekommunikation (DE-588)4341131-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4170280-3 (DE-588)4033447-8 (DE-588)4341131-9 (DE-588)4193754-5 |
| title | Machine learning and wireless communications |
| title_auth | Machine learning and wireless communications |
| title_exact_search | Machine learning and wireless communications |
| title_exact_search_txtP | Machine learning and wireless communications |
| title_full | Machine learning and wireless communications edited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University |
| title_fullStr | Machine learning and wireless communications edited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University |
| title_full_unstemmed | Machine learning and wireless communications edited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University |
| title_short | Machine learning and wireless communications |
| title_sort | machine learning and wireless communications |
| topic | Wireless communication systems Machine learning Mobilfunk (DE-588)4170280-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Mobile Telekommunikation (DE-588)4341131-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Wireless communication systems Machine learning Mobilfunk Künstliche Intelligenz Mobile Telekommunikation Maschinelles Lernen |
| url | https://doi.org/10.1017/9781108966559 |
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