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024 7 $a10.1007/978-3-030-10546-4
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035   $a(MiAaPQ)EBC5639444
035   $a(DE-He213)978-3-030-10546-4
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035   $a(EXLCZ)994100000007522458
100   $a20190117d2019      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDeep Reinforcement Learning for Wireless Networks /$fby F. Richard Yu, Ying He
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (78 pages)
225 1 $aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112
311   $a3-030-10545-8 
330   $aThis Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool. .
410  0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112
606   $aWireless communication systems
606   $aMobile communication systems
606   $aArtificial intelligence
606   $aElectrical engineering
606   $aWireless and Mobile Communication$3https://scigraph.springernature.com/ontologies/product-market-codes/T24100
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035
615  0$aWireless communication systems.
615  0$aMobile communication systems.
615  0$aArtificial intelligence.
615  0$aElectrical engineering.
615 14$aWireless and Mobile Communication.
615 24$aArtificial Intelligence.
615 24$aCommunications Engineering, Networks.
676   $a006.31
676   $a006.31
700   $aYu$b F. Richard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0864694
702   $aHe$b Ying$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910337637703321
996   $aDeep Reinforcement Learning for Wireless Networks$91930060
997   $aUNINA