LEADER 04328nam 22006495 450 001 9910366599603321 005 20200705082610.0 010 $a981-15-1120-9 024 7 $a10.1007/978-981-15-1120-2 035 $a(CKB)4100000009837015 035 $a(DE-He213)978-981-15-1120-2 035 $a(MiAaPQ)EBC5973790 035 $a(EXLCZ)994100000009837015 100 $a20191106d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTowards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks $eA Reinforcement Learning Perspective /$fby Zhiyong Du, Bin Jiang, Qihui Wu, Yuhua Xu, Kun Xu 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 136 p. 45 illus., 42 illus. in color.) 311 $a981-15-1119-5 327 $aIntroduction -- Learning the Optimal Network with Handoff Constraint: MAB RL Based Network Selection -- Learning the Optimal Network with Context Awareness: Transfer RL Based Network Selection -- Meeting Dynamic User Demand with Transmission Cost Awareness: CT-MAB RL Based Network Selection -- Meeting Dynamic User Demand with Handoff Cost Awareness: MDP RL Based Network Handoff -- Matching Heterogeneous User Demands: Localized Cooperation Game and MARL based Network Selection -- Exploiting User Demand Diversity: QoE game and MARL Based Network Selection -- Future Work. 330 $aThis book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond. 606 $aWireless communication systems 606 $aMobile communication systems 606 $aComputer communication systems 606 $aElectrical engineering 606 $aInformation theory 606 $aWireless and Mobile Communication$3https://scigraph.springernature.com/ontologies/product-market-codes/T24100 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aInformation and Communication, Circuits$3https://scigraph.springernature.com/ontologies/product-market-codes/M13038 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 0$aComputer communication systems. 615 0$aElectrical engineering. 615 0$aInformation theory. 615 14$aWireless and Mobile Communication. 615 24$aComputer Communication Networks. 615 24$aCommunications Engineering, Networks. 615 24$aInformation and Communication, Circuits. 676 $a384.5 700 $aDu$b Zhiyong$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063139 702 $aJiang$b Bin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWu$b Qihui$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aXu$b Yuhua$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aXu$b Kun$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366599603321 996 $aTowards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks$92530700 997 $aUNINA