This is a great opportunity to mention the comprehensive view of Mobile Communications and Machine Learning called ‘Machine Learning for Mobile Communications (Industry 5.0)’. It targets relative newcomers to the subject area and spans system-level issues all the way down to power management and resource utilization at the user level. The book also gives an understanding of some new trends in machine learning, including the areas of research that are still critical and link the academic and commercial worlds.
For those who wish to read more, there is a book named “Machine Learning for Mobile Communications (Industry 5.0)” that explains the long-term perspectives of future mobile communications. Some significant aspects include resource management, protection, power management, and spectral management, among others, providing new approaches and viewpoints. The readers can learn from leading experts with an overview of system design and optimization techniques from across the globe.
In addition, the book introduces the designing and architectural concepts of the 5G New radio system based on the 3GPP documents. It also describes difficulties providing security, privacy, energy, and spectrum in 5G systems. Conducting theoretical and applied analysis together with the demonstration of modern trends in ML application within the field of mobile communication, as well as using the real-life examples of autoencoders and Q-learning the book “Machine Learning for Mobile Communications (Industry 5.0)” is a great source of information and potential solutions to modern challenges of mobile systems.
Machine Learning for Mobile Communications (Industry 5.0) Table of Contents:
- Introduction to 5G New Radio
- NR Physical Layer
- NR Layer 2 and Layer 3
- 4G and 5G NR Core Network Architecture
- 5G—Further Evolution
- Security and Privacy
- Traffic Prediction and Congestion Control Using Regression Models in Machine Learning for Cellular Technology
- Resource Allocation Optimization
- Reciprocated Bayesian-RNN Classifier-Based Mode Switching and Mobility Management in Mobile Networks
- Mobility Management through Machine Learning
- Applying Heuristic Methods to the Offloading Problem in Edge Computing
- AFt/VR Data Prediction and a Slicing Model for 5G Edge Computing
Who is this course for?
- Senior undergraduate students in electrical engineering, electronics, and communication engineering.
- Electrical and Electronics Engineering, Electronics and Communication Engineering post-graduate students.
- The audience of this paper will comprise academic researchers in electrical engineering, electronics, and communication engineering.
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