Radio Frequency Machine Learning : a practical deep learning perspective / Scott Kuzdeba.

Format:

Book

Author/Creator:

Kuzdeba, Scott.

Language:

English

Subjects (All):

Machine learning.

Radio frequency.

Physical Description:

1 online resource (265 pages)

Edition:

1st ed.

Place of Publication:

Norwood : Artech House, 2025.

Summary:

This book provides a comprehensive exploration of the application of machine learning techniques in the domain of radio frequency (RF) systems. It introduces foundational concepts of RF machine learning (RF ML), focusing on practical applications, deep learning methodologies, and various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book discusses data collection, preprocessing, training, clustering, waveform synthesis, and robust RF ML system design. It also covers advanced topics such as federated learning, continual learning, and edge computing, emphasizing their impact on wireless networks and RF systems. The author aims to provide researchers, engineers, students, and wireless technology enthusiasts with the tools to understand and implement machine learning techniques for optimizing and improving wireless systems. With practical examples and case studies, the book bridges traditional RF signal processing methods and modern machine learning approaches, shedding light on the future of wireless technology. Generated by AI.

Contents:

Intro

Radio Frequency Machine Learning: A Practical Deep Learning Perspective

Contents

Foreword

Acknowledgments

Chapter 1 Introduction

1.1 RF ML

1.2 Where to Apply Deep Learning in the RF Domain

1.3 Shifting from Traditional to ML Processing

1.4 Book Organization

1.5 RF ML Ecosystem

1.6 Future Directions

References

Chapter 2 RF ML Classification

2.1 RF ML: Applications and When to Use

2.2 Data for Classification

2.2.1 Labels and Metadata

2.2.2 Input Representation: Data Transformations and Preprocessing

2.2.3 Training, Validation, and Testing

2.2.4 Datasets and Data Formats

2.3 Algorithms and Architecture

2.3.1 Feature Extraction

2.3.2 Dilated Causal Convolutions

2.3.3 Classification Layers

2.3.4 Learning Methodologies and Loss Functions

2.4 Performance Assessment

2.4.1 Metrics

2.4.2 Operational Considerations

2.5 Architecture Studies

2.5.1 Inspiration from Computer Vision: CNNs

2.5.2 Inspiration from Automatic Speech Recognition: RNNs

2.5.3 RF-Designed Solutions: The RiftNet Architecture

2.5.4 Transformers and Other Architectures

2.6 Shifting from Classification to Regression

Chapter 3 RF ML Clustering

3.1 RF ML: Applications and When to Use

3.2 Data for Clustering

3.2.1 Unlabeled Data

3.2.2 Using Labeled Data

3.2.3 Training, Validation, and Testing

3.3 Algorithms and Architecture

3.3.1 Features and Feature Extraction

3.3.2 Latent Manifolds

3.3.3 Clustering

3.3.4 Training and Learning Methodologies

3.3.5 An Example

3.4 Offline Versus Online Operation

3.4.1 Training

3.4.2 Inference

3.5 Performance Assessment

3.5.1 Metrics

3.5.2 Visualization

3.5.3 Operational Considerations

3.6 Architecture Studies

3.6.1 Dimensionality Reduction and Clustering.

3.6.2 Learning Latent Representations for Clustering

3.6.3 Architectures with Inherent Clustering

3.6.4 Creating Stronger Ties to Traditional Clustering

3.7 Using Supervision in Unsupervised Training

3.7.1 Semi-Supervised Learning

3.7.2 Self-Supervised Learning

3.8 Segmentation

Chapter 4 Waveform Synthesis: A Generative Approach

4.1 RF ML: Applications and When to Use

4.2 Algorithms and Architecture

4.2.1 Features and Feature Extraction

4.2.2 Generative Models

4.2.3 Training and Learning Methodologies

4.3 Performance Assessment

4.3.1 Metrics

4.3.2 Hallucinations

4.3.3 Operational Considerations

4.4 Architecture Studies

4.4.1 RiftNet Reconstruction Model

4.4.2 GAN

4.4.3 Diffusion Model

4.5 Reinforcement Learning-Driven Design

4.6 Adversarial RF ML

Chapter 5 Designing for RF Systems

5.1 RF ML: Applications and When to Use

5.2 Streaming Operations

5.2.1 Real-Time Operation

5.2.2 Intra-Signal Integration

5.2.3 Inter-Signal Integration

5.3 Detection

5.4 Hybrid Solutions

5.4.1 Using Traditional Features

5.4.2 Integration with Traditional Processing

5.4.3 Combining with Other Applications

5.5 Considering the Environment and Scenario

5.6 Control

5.6.1 Reinforcement Learning

5.6.2 Controlling Receiver Scan Schedules with RL

5.6.3 Closed-Loop Adaptation

5.7 Multimodal Considerations

Chapter 6 Developing Robust RF ML Solutions

6.1 Trustworthy AI

6.2 Transfer Learning

6.3 Operational Challenges

6.3.1 Sources of Drift

6.3.2 Novel Environments

6.3.3 Unknowns: New Signals and Systems

6.4 Explainability

6.4.1 Post Hoc Explainability

6.4.2 Baked-In Explainability

6.4.3 What Level of Explainability Is Enough?

6.5 Confidence

Chapter 7 RF Data and Augmentation.

7.1 Challenges

7.2 RF ML Datasets

7.3 Collecting Data

7.4 Modeling, Simulation, and Synthetic Data Generation

7.5 Data Augmentation

7.5.1 Inspiration from Image Augmentations

7.5.2 Inspiration from Text Augmentations

7.5.3 RF Augmentations

7.6 Data Imbalance and Sampling Methods

Chapter 8 Edge, Federated, and Continual Learning

8.1 RF ML: Applications and When to Use

8.1.1 Computation and Latency of Offline Models

8.2 Efficient Algorithms (Tiny ML)

8.2.1 Pruning

8.2.2 Quantization

8.2.3 Decomposition

8.2.4 Knowledge Distillation

8.2.5 Light-Weight Model Design

8.3 A Note on Training

8.4 Federated Learning

8.5 Continual and Active Learning

8.6 Adaptive Control

8.7 Hardware

Appendix

A.1 Background

A.2 Artificial Neural Networks

A.3 Understanding What It Means to Learn

A.4 How Theory Impacts Design Decisions

Acronyms and Abbreviations

About the Author

Index.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9781685690342

1685690343

OCLC:

1507697316