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SpringerBriefs in Electrical and Computer Engineering

Deep Reinforcement Learning for Wireless Networks

Authors: Yu, F. Richard, He, Ying

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  • ISBN 978-3-030-10546-4
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About this book

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 (4 chapters)

Table of contents (4 chapters)

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-030-10546-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-10545-7
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Deep Reinforcement Learning for Wireless Networks
Authors
Series Title
SpringerBriefs in Electrical and Computer Engineering
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-10546-4
DOI
10.1007/978-3-030-10546-4
Softcover ISBN
978-3-030-10545-7
Series ISSN
2191-8112
Edition Number
1
Number of Pages
VIII, 71
Number of Illustrations
2 b/w illustrations, 26 illustrations in colour
Topics

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