Matches in SemOpenAlex for { <https://semopenalex.org/work/W3040926217> ?p ?o ?g. }
- W3040926217 endingPage "71" @default.
- W3040926217 startingPage "51" @default.
- W3040926217 abstract "The rapid introduction of mobile devices and the growing popularity of mobile applications and services create unprecedented infrastructure requirements for mobile and wireless networks. Future 5G systems are evolving to support growing mobile traffic, real-time accurate analytics, and flexible network resource management to maximize user experience. These tasks are challenging as mobile environments become increasingly complex, heterogeneous and evolving. One possible solution is to use advanced machine learning techniques to help cope with the growth of data and algorithm-based applications. The recent success of deep learning supports new and powerful tools that solve problems in this domain. In this chapter, we focus on how deep reinforcement learning should be integrated into the architecture of future wireless communication networks is presented." @default.
- W3040926217 created "2020-07-16" @default.
- W3040926217 creator A5012910363 @default.
- W3040926217 creator A5038229891 @default.
- W3040926217 creator A5058368361 @default.
- W3040926217 creator A5060490902 @default.
- W3040926217 date "2020-06-17" @default.
- W3040926217 modified "2023-10-10" @default.
- W3040926217 title "Deep Reinforcement Learning for Wireless Network" @default.
- W3040926217 cites W1505958606 @default.
- W3040926217 cites W1603438237 @default.
- W3040926217 cites W1973547261 @default.
- W3040926217 cites W1991539813 @default.
- W3040926217 cites W1995341919 @default.
- W3040926217 cites W2004802883 @default.
- W3040926217 cites W2020606754 @default.
- W3040926217 cites W2038383584 @default.
- W3040926217 cites W2040870580 @default.
- W3040926217 cites W2067507621 @default.
- W3040926217 cites W2068654401 @default.
- W3040926217 cites W2069601023 @default.
- W3040926217 cites W2076063813 @default.
- W3040926217 cites W2078626246 @default.
- W3040926217 cites W2083380015 @default.
- W3040926217 cites W2091005538 @default.
- W3040926217 cites W2103972037 @default.
- W3040926217 cites W2114936722 @default.
- W3040926217 cites W2115742196 @default.
- W3040926217 cites W2125890412 @default.
- W3040926217 cites W2128084896 @default.
- W3040926217 cites W2139465937 @default.
- W3040926217 cites W2145480127 @default.
- W3040926217 cites W2150339816 @default.
- W3040926217 cites W2161336914 @default.
- W3040926217 cites W2285924575 @default.
- W3040926217 cites W2346058309 @default.
- W3040926217 cites W2400829371 @default.
- W3040926217 cites W2466084743 @default.
- W3040926217 cites W2511063986 @default.
- W3040926217 cites W2562947506 @default.
- W3040926217 cites W2565516711 @default.
- W3040926217 cites W2576765166 @default.
- W3040926217 cites W2581508406 @default.
- W3040926217 cites W2586992378 @default.
- W3040926217 cites W2602923095 @default.
- W3040926217 cites W2605934746 @default.
- W3040926217 cites W2614329785 @default.
- W3040926217 cites W2617931713 @default.
- W3040926217 cites W2735793369 @default.
- W3040926217 cites W2800319120 @default.
- W3040926217 cites W2807731816 @default.
- W3040926217 cites W2919115771 @default.
- W3040926217 cites W2950929549 @default.
- W3040926217 cites W2963019788 @default.
- W3040926217 cites W2963049813 @default.
- W3040926217 cites W2963241379 @default.
- W3040926217 cites W2964254270 @default.
- W3040926217 cites W3099206234 @default.
- W3040926217 cites W3100789280 @default.
- W3040926217 cites W3100857292 @default.
- W3040926217 cites W65738273 @default.
- W3040926217 doi "https://doi.org/10.1002/9781119640554.ch3" @default.
- W3040926217 hasPublicationYear "2020" @default.
- W3040926217 type Work @default.
- W3040926217 sameAs 3040926217 @default.
- W3040926217 citedByCount "1" @default.
- W3040926217 countsByYear W30409262172022 @default.
- W3040926217 countsByYear W30409262172023 @default.
- W3040926217 crossrefType "other" @default.
- W3040926217 hasAuthorship W3040926217A5012910363 @default.
- W3040926217 hasAuthorship W3040926217A5038229891 @default.
- W3040926217 hasAuthorship W3040926217A5058368361 @default.
- W3040926217 hasAuthorship W3040926217A5060490902 @default.
- W3040926217 hasConcept C107457646 @default.
- W3040926217 hasConcept C108037233 @default.
- W3040926217 hasConcept C108583219 @default.
- W3040926217 hasConcept C120314980 @default.
- W3040926217 hasConcept C144543869 @default.
- W3040926217 hasConcept C153646914 @default.
- W3040926217 hasConcept C154945302 @default.
- W3040926217 hasConcept C15744967 @default.
- W3040926217 hasConcept C2780586970 @default.
- W3040926217 hasConcept C31258907 @default.
- W3040926217 hasConcept C41008148 @default.
- W3040926217 hasConcept C555944384 @default.
- W3040926217 hasConcept C76155785 @default.
- W3040926217 hasConcept C77805123 @default.
- W3040926217 hasConcept C97541855 @default.
- W3040926217 hasConceptScore W3040926217C107457646 @default.
- W3040926217 hasConceptScore W3040926217C108037233 @default.
- W3040926217 hasConceptScore W3040926217C108583219 @default.
- W3040926217 hasConceptScore W3040926217C120314980 @default.
- W3040926217 hasConceptScore W3040926217C144543869 @default.
- W3040926217 hasConceptScore W3040926217C153646914 @default.
- W3040926217 hasConceptScore W3040926217C154945302 @default.
- W3040926217 hasConceptScore W3040926217C15744967 @default.
- W3040926217 hasConceptScore W3040926217C2780586970 @default.
- W3040926217 hasConceptScore W3040926217C31258907 @default.