Deep Reinforcement Learning for Wireless Networks (Paperback, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Electrical and Computer Engineering
Release date:	2019
First published:	2019
Authors:	F. Richard Yu • Ying He
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	71
Edition:	1st ed. 2019
ISBN-13:	978-3-03-010545-7
Categories:	Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > WAP (wireless) technology Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-010545-8
Barcode:	9783030105457

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