Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159845
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dc.contributor.authorXiong, Zehuien_US
dc.contributor.authorZhao, Junen_US
dc.contributor.authorNiyato, Dusiten_US
dc.contributor.authorDeng, Ruilongen_US
dc.contributor.authorZhang, Junshanen_US
dc.date.accessioned2022-07-04T07:37:55Z-
dc.date.available2022-07-04T07:37:55Z-
dc.date.issued2020-
dc.identifier.citationXiong, Z., Zhao, J., Niyato, D., Deng, R. & Zhang, J. (2020). Reward optimization for content providers with mobile data subsidization: a hierarchical game approach. IEEE Transactions On Network Science and Engineering, 7(4), 2363-2377. https://dx.doi.org/10.1109/TNSE.2020.3016963en_US
dc.identifier.issn2327-4697en_US
dc.identifier.urihttps://hdl.handle.net/10356/159845-
dc.description.abstractMobile data subsidization launched by mobile network operators is a promising business model to provide economic benefits for the mobile data market and beyond. It allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. From a content provider perspective, it is of great interest to determine the optimal strategy for offering appropriate data subsidization (reward) in order to compete against others to earn more revenue and gain higher profit. In this paper, we take a hierarchical game approach to model the reward optimization process for the content providers. To analyze the relationship between the provider and the user, we first focus on the one-to-one interaction in a single-provider single-user system, and formulate a Mathematical Program with Equilibrium Constraints (MPEC). We apply the backward induction to solve the MPEC problem and prove the existence and uniqueness of the Stackelberg equilibrium. We then formulate an Equilibrium Program with Equilibrium Constraints (EPEC) to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexity of the EPEC problem, we utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipEnergy Market Authority (EMA)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationRG128/18en_US
dc.relationRG115/19en_US
dc.relationRT07/19en_US
dc.relationRT01/19en_US
dc.relationMOE2019-T2-1-176en_US
dc.relationNSoE DeST-SCI2019-0007en_US
dc.relationNRF2017EWT-EP003-041en_US
dc.relationNRF2015-NRF-ISF001-2277en_US
dc.relationRGANS1906en_US
dc.relationM4082187 (4080)en_US
dc.relationRG16/20en_US
dc.relation.ispartofIEEE Transactions on Network Science and Engineeringen_US
dc.rights© 2020 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleReward optimization for content providers with mobile data subsidization: a hierarchical game approachen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchAlibaba-NTU Singapore Joint Research Instituteen_US
dc.identifier.doi10.1109/TNSE.2020.3016963-
dc.identifier.scopus2-s2.0-85100751629-
dc.identifier.issue4en_US
dc.identifier.volume7en_US
dc.identifier.spage2363en_US
dc.identifier.epage2377en_US
dc.subject.keywordsGamesen_US
dc.subject.keywordsOptimizationen_US
dc.description.acknowledgementThe work of Zehui Xiong is supported by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore. The work of Jun Zhao is supported by 1) Nanyang Technological University (NTU) Startup Grant, 2) Alibaba-NTU Singapore Joint Research Institute (JRI), 3) Singapore Ministry of Education Academic Research Fund Tier 1 RG128/18, Tier 1 RG115/19, Tier 1 RT07/19, Tier 1 RT01/19, and Tier 2 MOE2019-T2-1-176, 4) NTU-WASP Joint Project, 5) Singapore National Research Foundation (NRF) under its Strategic Capability Research Centres Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS), 6) Energy Research Institute @NTU (ERIAN), 7) Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoEDeST-SCI2019-0012, 8) AI Singapore (AISG) 100 Experiments (100E) programme, and 9) NTU Project for Large Vertical Take-Off & Landing (VTOL) Research Platform. The work of Dusit Niyato is supported by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWT-EP003-041, Singapore NRF2015-NRF-ISF001-2277, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing RGANS1906, Wallenberg AI, Autonomous Systems and Software Programand Nanyang Technological University (WASP/NTU) under grant M4082187 (4080), Singapore Ministry of Education (MOE) Tier 1 (RG16/20), and Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI). The work of Ruilong Deng was supported in part by the National Natural Science Foundation of China under Grant 61873106 and 62061130220, and in part by the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform).en_US
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