Название: Machine Learning for Wireless CommunicationАвтор: Rohit M. Thanki, Komal R. Borisagar, Anjali Diwan
Издательство: Springer
Год: 2025
Страниц: 130
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
This book covers the basic principles of wireless communication while delving into the fundamentals of Machine Learning, including supervised and unsupervised learning, Deep Learning, and Reinforcement Learning. The authors provide real-world examples and case studies to illustrate the use of Machine Learning in wireless communication applications such as channel estimation, mobility prediction, resource allocation, and beamforming. This book is an essential resource for researchers, engineers, and students interested in understanding and applying Machine Learning techniques in the context of wireless communication systems.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention. Unlike traditional programming, where rules are explicitly coded, ML systems are trained using data to improve their performance on a specific task over time. The key components in Machine Learning include the following:
- Algorithms: The set of rules or instructions followed by the ML system to learn from data and make decisions. Examples include decision trees, logistic regression, neural networks, etc.
- Training dаta: A dataset used to train the ML model, consisting of input–output pairs. The quality and quantity of training data significantly affect the model’s performance.
- Model: The mathematical representation learned from the training data, which can be used to make predictions or decisions based on new data.
Signal detection and classification are crucial tasks in wireless communication systems, enabling the identification and characterization of signals in complex and noisy environments. The application of Machine Learning (ML) techniques has significantly improved the accuracy and efficiency of these tasks, facilitating advanced communication technologies.
Chapter 1 lays the foundational groundwork, offering an overview of wireless communication and key ML concepts. It introduces the motivations for integrating ML into wireless systems and highlights current trends through real-world case studies.
Chapter 2 delves into signal processing applications, including detection, channel modeling, and interference mitigation, showcasing how ML algorithms can outperform classical methods in dynamic environments.
Chapter 3 addresses network-level optimization, focusing on intelligent resource management, traffic forecasting, and energy-efficient communication—all critical to the sustainability and scalability of future networks.
Chapter 4 explores the growing importance of security in wireless networks, examining how ML techniques can enhance threat detection, anomaly identification, secure protocols, and privacy preservation.
Chapter 5 looks toward the future, covering cutting-edge topics such as edge and fog computing, cognitive radio networks, and the Internet of Things (IoT). It also presents a forward-looking perspective on 6G networks, ethical considerations, and the broader societal impacts of deploying ML in wireless infrastructures.
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