Deep Reinforcement Learning for Wireless Networks

✍ Scribed by F. Richard Yu, Ying He

Publisher: Springer International Publishing
Year: 2019
Tongue: English
Leaves: 78
Series: SpringerBriefs in Electrical and Computer Engineering
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results..

Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.

✦ Table of Contents

Front Matter ....Pages i-viii
Introduction to Machine Learning (F. Richard Yu, Ying He)....Pages 1-13
Reinforcement Learning and Deep Reinforcement Learning (F. Richard Yu, Ying He)....Pages 15-19
Deep Reinforcement Learning for Interference Alignment Wireless Networks (F. Richard Yu, Ying He)....Pages 21-44
Deep Reinforcement Learning for Mobile Social Networks (F. Richard Yu, Ying He)....Pages 45-71

✦ Subjects

Engineering; Wireless and Mobile Communication; Communications Engineering, Networks

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