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020			_a9789811511202
024	7		_a10.1007/978-981-15-1120-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5103.2 _b2020 EB
100	1		_aDu, Zhiyong _9672575
245	1	0	_aTowards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks : _bA Reinforcement Learning Perspective _cby Zhiyong Du, Bin Jiang, Qihui Wu, Yuhua Xu, Kun Xu
250			_aPrimera edición 2020
264		1	_aSingapore _bSpringer _c2020
300			_a1 recurso en línea (XII, 136 páginas) _b 45 ilustraciones, 42 ilustraciones a color.
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_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.
520	3		_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.
988			_aPrimersemestre_2020_Engineering
650		7	_2embne _aSistemas de comunicación móviles _9147646
700	1		_aJiang, Bin _eautor
700	1		_aWu, Qihui _eautor
700	1		_aXu, Yuhua _eautor _9100721
700	1		_aXu, Kun _eautor
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789811511196
776	0	8	_iPrinted edition: _z9789811511219
776	0	8	_iPrinted edition: _z9789811511226
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-1120-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eu _zSI