Details

Deep Reinforcement Learning for Wireless Networks


Deep Reinforcement Learning for Wireless Networks


SpringerBriefs in Electrical and Computer Engineering

von: F. Richard Yu, Ying He

64,19 €

Verlag: Springer
Format: PDF
Veröffentl.: 17.01.2019
ISBN/EAN: 9783030105464
Sprache: englisch

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<p>This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance.&nbsp;Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless&nbsp;networks and mobile social networks. Simulation results with different network parameters are&nbsp;presented to show the effectiveness of the proposed scheme.</p>

<p>&nbsp;There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement&nbsp;learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent&nbsp;projects with big data (e.g., AlphaGo), and gets quite good results..</p>

<p>&nbsp;Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer&nbsp;scientists, programmers, and policy makers will also find this brief to be a useful tool.&nbsp;</p><p></p><p></p>

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