Introduction.- 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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Zhiyong Du received his B.S. degree in Electronic Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and his Ph.D. degree in Communications and Information Systems from the College of Communications Engineering, PLA University of Science and Technology, Nanjing, China, in 2015. He is currently a lecturer at the National University of Defense Technology. His research interests include 5G, quality of experience (QoE), learning theory, and game theory.
Bin Jiang received his B.S. degree in Communication Engineering and Ph.D. degree in Information and Communication Engineering both from the National University of Defense Technology, Changsha, China, in 1996 and 2006, respectively. He is currently a Professor at the National University of Defense Technology. His research interests include 5G, artificial intelligence, and wireless signal processing.
Qihui Wu received his B.S., M.S., and Ph.D. degrees in Communications and Information Systems from the PLA University of Science and Technology, Nanjing, China, in 1994, 1997, and 2000, respectively. He is Professor at the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. His current research interests include algorithms and optimization for cognitive wireless networks, software-defined radio, and wireless communication systems.
Yuhua Xu received his B.S. degree in Communication Engineering and Ph.D. degree in Communications and Information Systems from the College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014, respectively. He is currently an Associate Professor at the College of Communications Engineering, Army Engineering University of PLA. He has published several papers in international conferences and respected journals. His research interests include UAV communication networks, opportunistic spectrum access, learning theory, and distributed optimization techniques for wireless communications. He received a Certificate of Appreciation as an Exemplary Reviewer of the IEEE Communications Letters, in 2011 and 2012. He received the IEEE Signal Processing Society 2015 Young Author Best Paper Award and the Funds for Distinguished Young Scholars of Jiangsu Province in 2016.
Kun Xu received his B.S. degree in Communication Engineering and Ph. D. degree in Communications and Information Systems, both from PLA University of Science and Technology, in 2007 and 2013, respectively. He is currently a lecturer at the College of Information and Communication, National University of Defense Technology (NUDT). His research interests include HF communication, unmanned aerial vehicle communication, and relay communication.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
EUR 30 expédition depuis Allemagne vers Etats-Unis
Destinations, frais et délaisEUR 3,42 expédition vers Etats-Unis
Destinations, frais et délaisVendeur : Universitätsbuchhandlung Herta Hold GmbH, Berlin, Allemagne
XII, 136 p. Hardcover. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch. N° de réf. du vendeur 43715HB
Quantité disponible : 3 disponible(s)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Etat : New. N° de réf. du vendeur ABLIING23Apr0412070087917
Quantité disponible : Plus de 20 disponibles
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Hardcover. Etat : new. Hardcover. This 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. This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. 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). Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9789811511196
Quantité disponible : 1 disponible(s)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9789811511196_new
Quantité disponible : Plus de 20 disponibles
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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. 148 pp. Englisch. N° de réf. du vendeur 9789811511196
Quantité disponible : 2 disponible(s)
Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. N° de réf. du vendeur 26384117374
Quantité disponible : 4 disponible(s)
Vendeur : moluna, Greven, Allemagne
Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers new insights into how to model and exploit user demand in resource managementProvides various application examples of reinforcement learning algorithms on resource management of wireless networksPresents novel game models and associa. N° de réf. du vendeur 319503024
Quantité disponible : Plus de 20 disponibles
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand. N° de réf. du vendeur 378705313
Quantité disponible : 4 disponible(s)
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18384117364
Quantité disponible : 4 disponible(s)
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Hardcover. Etat : Brand New. 150 pages. 9.25x6.10x0.55 inches. In Stock. N° de réf. du vendeur x-9811511195
Quantité disponible : 2 disponible(s)