Radio Frequency Machine Learning A Practical Deep Learning Perspective
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Radio Frequency Machine Learning: A Practical Deep Learning Perspective
Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification tasks where deep learning is used to label and categorize signals based on a labeled training dataset, as well as clustering tasks that group similar signals together without labels. Additionally, it expands into deep learning (generative AI) for waveform synthesis and how reinforcement learning can be used within the domain. This book also investigates advanced topics like RF sensor control, feedback mechanisms, and real-time system operations, offering a comprehensive understanding of how deep learning can be integrated into dynamic RF environments. This resource addresses the practical concerns of deploying machine learning in operational RF systems. It goes beyond applications and techniques, covering how to ensure the robustness of solutions, with insights into data sources, augmentation techniques, and strategies for integrating ML with existing RF infrastructure. The full development process is examined, from data collection to deployment, along with numerous case studies throughout. Looking to the future, the book explores emerging trends like edge computing and federated learning, offering a forward-looking perspective on the continued evolution of RF machine learning. Whether the reader is just beginning the journey into RF machine learning or is looking to refine skills, this book provides an essential resource for understanding the intersection of deep learning and RF technology. This is a must-have resource for anyone interested in the cutting edge of wireless technologies and their potential to shape the future of communication.
Deep Learning for Radar and Communications Automatic Target Recognition
This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.
Machine Learning in Signal Processing
Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.