Incentive Mechanism for Resource Allocation in Wireless Virtualized Networks with Multiple Infrastructure Providers
Chang, Z., Zhang, D., Hämäläinen, T., Han, Z., & Ristaniemi, T. (2020). Incentive Mechanism for Resource Allocation in Wireless Virtualized Networks with Multiple Infrastructure Providers. IEEE Transactions on Mobile Computing, 19(1), 103-115. https://doi.org/10.1109/TMC.2018.2889046
Published inIEEE Transactions on Mobile Computing
© 2018 IEEE
To accommodate the explosively growing demands for mobile traffic service, wireless network virtualization is proposed as the main evolution towards 5G. In this work, a novel contract theoretic incentive mechanism is proposed to study how to manage the resources and provide services to the users in the wireless virtualized networks. We consider that the infrastructure providers (InPs) own the physical networks and the mobile virtual network operator (MVNO) has the service information of the users and needs to lease the physical radio resources for providing services. In particular, we utilize the contract theoretic approach to model the resource trading process between the MVNO and multiple InPs. Two scenarios are considered according to whether the information (such as the radio resource they can provide) of the InPs are globally known. Subsequently, the corresponding optimal contracts regarding the user association and transmit power allocation are derived to maximize the payoff of the MVNOs while maintaining the requirements of the InPs in the trading process. To evaluate the proposed scheme, extensive simulation studies are conducted. It can be observed that the proposed contract theoretic approach can effectively stimulate InPs’ participation, improve the payoff of the MVNO, and outperform other schemes. ...
PublisherInstitute of Electrical and Electronics Engineers
Publication in research information system
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Additional information about fundingThe research is partially supported by US MURI AFOSR MURI 18RT0073, and US National Science Foundation CNS-1717454, CNS-1731424, CNS-1702850, CNS-1646607, and ECCS-1547201.
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