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An Oblivious Game-Theoretic Approach for Wireless Scheduling in V2V Communications

机译:V2V通信中无线调度的一种遗忘博弈论方法

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This paper addresses the problem of wireless resource scheduling in a vehicle-to-vehicle (V2V) communication network. The technical challenges lie in the fast changing network dynamics, namely, the channel quality and the data traffic variations. For a road segment covered by a road side unit (RSU), especially in a dense urban area, the vehicle density tends to be stable. The incoming service requests from the vehicle user equipment (VUE)-pairs compete with each other for the limited frequency resource in order to deliver data packets. Such competitions are regulated by the RSU via a sealed second-price auction at the beginning of scheduling slots. Each incumbent service request aims at maximizing the expected long-term payoff from bidding the frequency resource for packet transmissions. Markov perfect equilibrium (MPE) can be utilized to characterize the optimal competitive behaviors of the service requests. When the number of incumbent VUE-pairs becomes large, solving the MPE becomes infeasible. We adopt an oblivious equilibrium to approximate the MPE, which is theoretically proven to be error-bounded. The decision making process at each service request is hence transformed into a single-agent Markov decision process, for which we propose an on-line auction based learning scheme. Through simulation experiments, we show the potential performance gains from our proposed scheme, in terms of per-service request average utility.
机译:本文解决了车对车(V2V)通信网络中的无线资源调度问题。技术挑战在于快速变化的网络动态,即信道质量和数据流量变化。对于被路侧单元(RSU)覆盖的路段,尤其是在人口稠密的市区中,车辆密度趋于稳定。来自车辆用户设备(VUE)对的传入服务请求彼此竞争有限的频率资源,以便传递数据包。 RSU通过在预定时段开始时进行密封的第二价格拍卖来监管此类竞争。每个现有服务请求的目的是使从竞标频率资源进行分组传输时获得的预期长期收益最大化。马尔可夫完美均衡(MPE)可用于表征服务请求的最佳竞争行为。当现有的VUE对的数量变大时,解决MPE变得不可行。我们采用遗忘平衡来近似MPE,理论上证明该误差是有误差的。因此,将每个服务请求的决策过程转换为单代理马尔可夫决策过程,为此,我们提出了一种基于在线拍卖的学习方案。通过仿真实验,我们展示了根据每项服务请求的平均效用,我们提出的方案可能带来的性能提升。

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