Deep Reinforcement Learning for Wireless Networks - F. Richard Yu, Ying He

Deep Reinforcement Learning for Wireless Networks

, (Autoren)

Buch | Softcover
VIII, 71 Seiten
2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-10545-7 (ISBN)
64,19 inkl. MwSt
lt;p>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
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
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