Next-Generation Wireless Networks Meet Advanced Machine Learning Applications

  • 7h 44m
  • Ioan-Sorin Comşa, Ramona Trestian
  • IGI Global
  • 2019

The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand?

Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

About the Author

Ramona Trestian is a Lecturer with the Computer and Communications Engineering Department, School of Science and Technology, Middlesex University, London, UK. She was previously an IBM-IRCSET Exascale Postdoctoral Researcher with the Performance Engineering Laboratory (PEL) at Dublin City University (DCU), Ireland since December 2011. She was awarded the PhD from Dublin City University in March 2012 and the B.Eng. degree in Telecommunications from the Electronics, Telecommunications, and the Technology of Information Department, Technical University of Cluj-Napoca, Romania in 2007. She has published in prestigious international conferences and journals and has two edited books. She is a reviewer for international journals and conferences and an IEEE member. Her research interests include mobile and wireless communications, multimedia streaming, handover and network selection strategies, and software-defined networks.

In this Book

  • The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks
  • Machine Learning in Radio Resource Scheduling
  • Machine Learning for Internet of Things
  • A Survey on Routing Protocols of Wireless Sensor Networks: A Reliable Data Transfer Using Multiple Sink for Disaster Management
  • Review: Effective Solutions for Challenges in Cognitive Radio Networks
  • Overview of Machine Learning Approaches for Wireless Communication
  • Machine Learning in Wireless Communication: A Survey
  • Guaranteeing User Rates with Reinforcement Learning in 5G Radio Access Networks
  • Online Learning and Heuristic Algorithms for 5G Cloud-RAN Load Balance
  • Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks
  • Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks
  • Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems
  • Cheerbot: A Step Ahead of Conventional ChatBot


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