Book contents
- Frontmatter
- Dedication
- Contents
- Contributors
- Preface
- 1 Machine Learning and Communications: An Introduction
- Part I Machine Learning for Wireless Networks
- 2 Deep Neural Networks for Joint Source-Channel Coding
- 3 Neural Network Coding
- 4 Channel Coding via Machine Learning
- 5 Channel Estimation, Feedback, and Signal Detection
- 6 Model-Based Machine Learning for Communications
- 7 Constrained Unsupervised Learning for Wireless Network Optimization
- 8 Radio Resource Allocation in Smart Radio Environments
- 9 Reinforcement Learning for Physical Layer Communications
- 10 Data-Driven Wireless Networks: Scalability and Uncertainty
- 11 Capacity Estimation Using Machine Learning
- Part II Wireless Networks for Machine Learning
- Index
9 - Reinforcement Learning for Physical Layer Communications
from Part I - Machine Learning for Wireless Networks
Published online by Cambridge University Press: 16 June 2022
Book contents
- Frontmatter
- Dedication
- Contents
- Contributors
- Preface
- 1 Machine Learning and Communications: An Introduction
- Part I Machine Learning for Wireless Networks
- 2 Deep Neural Networks for Joint Source-Channel Coding
- 3 Neural Network Coding
- 4 Channel Coding via Machine Learning
- 5 Channel Estimation, Feedback, and Signal Detection
- 6 Model-Based Machine Learning for Communications
- 7 Constrained Unsupervised Learning for Wireless Network Optimization
- 8 Radio Resource Allocation in Smart Radio Environments
- 9 Reinforcement Learning for Physical Layer Communications
- 10 Data-Driven Wireless Networks: Scalability and Uncertainty
- 11 Capacity Estimation Using Machine Learning
- Part II Wireless Networks for Machine Learning
- Index
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Machine Learning and Wireless Communications , pp. 231 - 284Publisher: Cambridge University PressPrint publication year: 2022