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Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032306947 ISBN 13: 9781032306940
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Paperback. Zustand: new. Paperback. Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.This book:Discusses the 5G new radio system design and architecture as specified in 3GPP documentsHighlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systemsIdentifies both theoretical and practical problems that can occur in mobile communication systemsCovers machine learning techniques such as autoencoder and Q-learning in a comprehensive mannerExplores how to apply machine learning techniques to mobile systems to solve modern problemsThis book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. Machine Learning for Mobile Communication takes readers on a journey from the basic to advanced knowledge about mobile communications and machine learning. It is for undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 1032306947 ISBN 13: 9781032306940
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In den WarenkorbPaperback. Zustand: new. Paperback. Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.This book:Discusses the 5G new radio system design and architecture as specified in 3GPP documentsHighlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systemsIdentifies both theoretical and practical problems that can occur in mobile communication systemsCovers machine learning techniques such as autoencoder and Q-learning in a comprehensive mannerExplores how to apply machine learning techniques to mobile systems to solve modern problemsThis book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. Machine Learning for Mobile Communication takes readers on a journey from the basic to advanced knowledge about mobile communications and machine learning. It is for undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Sinh Cong Lam received a Bachelor of Electronics and Telecommunication (Honours) and Master of Electronic Engineering in 2010 and 2012, respectively, from University of Engineering and Technology, Vietnam National University (UET, VNUH).
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning for Mobile Communication takes readers on a journey from the basic to advanced knowledge about mobile communications and machine learning. It is for undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032306947 ISBN 13: 9781032306940
Anbieter: AussieBookSeller, Truganina, VIC, Australien
Paperback. Zustand: new. Paperback. Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.This book:Discusses the 5G new radio system design and architecture as specified in 3GPP documentsHighlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systemsIdentifies both theoretical and practical problems that can occur in mobile communication systemsCovers machine learning techniques such as autoencoder and Q-learning in a comprehensive mannerExplores how to apply machine learning techniques to mobile systems to solve modern problemsThis book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. Machine Learning for Mobile Communication takes readers on a journey from the basic to advanced knowledge about mobile communications and machine learning. It is for undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Machine Learning for Mobile Communications | Sinh Cong Lam (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032306940 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Sinh Cong Lam received a Bachelor of Electronics and Telecommunication (Honours) and Master of Electronic Engineering in 2010 and 2012, respectively, from University of Engineering and Technology, Vietnam National University (UET, VNUH).
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