Deep Reinforcement Learning for Wireless Networks

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...

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Bibliographic Details
Main Authors: Yu, F. Richard (Author), He, Ying (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:SpringerBriefs in Electrical and Computer Engineering,
Subjects:
Wireless communication systems.
Mobile communication systems.
Artificial intelligence.
Telecommunication.
Wireless and Mobile Communication.
Artificial Intelligence.
Communications Engineering, Networks.
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