Textbook in PDF format

How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications ? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
List of Contributors page.
Preface.
Machine Learning and Communications: An Introduction.
Machine Learning for Wireless Networks
Deep Neural Networks for Joint Source-Channel Coding.
Neural Network Coding.
Channel Coding via Machine Learning.
Channel Estimation, Feedback, and Signal Detection.
Model-Based Machine Learning for Communications.
Constrained Unsupervised Learning for Wireless Network Optimization.
Radio Resource Allocation in Smart Radio Environments.
Reinforcement Learning for Physical Layer Communications.
Data-Driven Wireless Networks: Scalability and Uncertainty.
Capacity Estimation Using Machine Learning.
Wireless Networks for Machine Learning
Collaborative Learning over Wireless Networks: An Introductory Overview.
Optimized Federated Learning in Wireless Networks with Constrained Resources.
Quantized Federated Learning.
Over-the-Air Computation for Distributed Learning over Wireless Networks.
Federated Knowledge Distillation.
Differentially Private Wireless Federated Learning.
Timely Wireless Edge Inference.
Index