Deep Reinforcement Learning for Wireless Networks

F. Richard Yu, Ying He (Autoren)

Buch | Softcover

VIII, 71 Seiten

2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-10545-7 (ISBN)

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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.

Erscheinungsdatum	31.01.2019
Reihe/Serie	SpringerBriefs in Electrical and Computer Engineering
Zusatzinfo	VIII, 71 p. 28 illus., 26 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	136 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Nachrichtentechnik
Schlagworte	Artificial Intelligence • Caching • connected vehicular networks • Deep learning • Deep Reinforcement Learning • interference alginment • machine learning • Mobile Edge Computing • Mobile Social Networks • Optimization • Reinforcement Learning • Resource Allocation • tensorflow • wireless networks • Wireless systems
ISBN-10	3-030-10545-8 / 3030105458
ISBN-13	978-3-030-10545-7 / 9783030105457
Zustand	Neuware