Graph-based optimal revenue packet scheduling in Vehicle-to-Infrastructure communication

2017 IEEE International Conference on Communications Workshops (ICC Workshops) Pub Date : 2017-05-01 DOI:10.1109/ICCW.2017.7962721

Yanyan Lu, Qimei Cui, Yanzhao Hou, Zhen-guo Gao, Yuhao Zhang

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引用次数: 1

Abstract

Recently, LTE-based Vehicle-to-Infrastructure (V2I) communication is widely studied due to its considerable potential to satisfy users' Quality of Service (QoS) requirements. It is convenient within the diversity of infrastructures. In particular, packet scheduling is of great importance in V2I. In this paper, we establish an optimization packet scheduling model in V2I according to the user's requests including lifetime, the number of needed packets and individual costs. Then, it is designed where the cost of each packet obeys linear decreasing function in a given time interval in order to be closer to the reality. Furthermore, we divide requirements with respect to the number of desired data packets to transform the packet delivery problem to a maximum weight problem in bipartite graph. At the same time, Kuhn-Munkres (KM) algorithm is adopted to maximize the revenue while reducing the complexity. The simulation results show that our proposed algorithm is effective both in offline and online case which increases 52.31% overall revenue while reducing 46.80% CPU time.

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基于图的车对基础通信收益包调度

近年来，基于lte的车对基础设施(V2I)通信因其在满足用户服务质量(QoS)需求方面的巨大潜力而受到广泛研究。它在基础设施的多样性中很方便。特别是，分组调度在V2I中非常重要。在本文中，我们根据用户的需求，包括使用寿命、需要的数据包数量和个体成本，建立了V2I中的优化数据包调度模型。然后，为了更接近实际，设计了在给定的时间间隔内，每个数据包的代价服从线性递减函数的算法。进一步，我们根据所需数据包的数量划分需求，将数据包传递问题转化为二部图中的最大权值问题。同时，采用KM (Kuhn-Munkres)算法，在降低复杂度的同时实现收益最大化。仿真结果表明，该算法在离线和在线情况下都是有效的，总体收益增加52.31%，CPU时间减少46.80%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 IEEE International Conference on Communications Workshops (ICC Workshops)

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