期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 10, 页码 10853-10863出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3106059
关键词
Device-to-device communication; Cellular networks; Encoding; Economics; Quality of experience; Games; Media Access Protocol; D2D communication; content delivery; Nash bargaining; incentive mechanism; one-to-many bargaining
This article investigates the economic interaction between a single base station and multiple content owners for content delivery, and designs a fair incentive mechanism to motivate content owners for content delivery.
Multi-source device-to-device (D2D) communication allows the base station (BS) to serve the content requests of the users locally through D2D links. As a result, the load on the base stations (BSs) and the consumption of radio resources reduces significantly. Clearly, the success of multi-source content delivery relies on the willingness of the content owners (COs), i.e., sources, to deliver their content to requesting user. Consequently, in this article, we investigate the economic interaction between single BS and multiple COs for content delivery. In view of the fact that participating COs are heterogeneous with respect to the amount of content in their cache and the sensitivity towards energy consumption, there is a need to design a fair incentive mechanism which motivates COs for content delivery. To this end, we model the interaction among the BS and multiple COs as one-to-many bargaining game and design an incentive mechanism based on the Nash bargaining framework. Specifically, we obtain the optimal amount of content delivered by participating COs and their corresponding incentives under two variants of one-to-many bargaining, namely sequential bargaining and concurrent bargaining. For both variants of bargaining, the obtained optimal solutions are capable of minimizing the amount of content delivered by the BS while ensuring fair incentive transfer among the participating COs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据