Sinh Cong Lam – författare

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 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 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 documents Highlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systems Identifies both theoretical and practical problems that can occur in mobile communication systems Covers machine learning techniques such as autoencoder and Q-learning in a comprehensive manner Explores how to apply machine learning techniques to mobile systems to solve modern problems

This book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.