Deep Reinforcement Learning for Wireless Networks

  • F. Richard Yu
  • Ying He

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-viii
  2. F. Richard Yu, Ying He
    Pages 1-13
  3. F. Richard Yu, Ying He
    Pages 15-19
  4. F. Richard Yu, Ying He
    Pages 45-71

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. 


Deep reinforcement learning reinforcement learning deep learning wireless networks caching mobile edge computing machine learning interference alginment connected vehicular networks TensorFlow resource allocation Artificial Intelligence Mobile Social Networks Wireless Systems Optimization

Authors and affiliations

  • F. Richard Yu
    • 1
  • Ying He
    • 2
  1. 1.Carleton UniversityOttawaCanada
  2. 2.Carleton UniversityOttawaCanada

Bibliographic information

  • DOI
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-10545-7
  • Online ISBN 978-3-030-10546-4
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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