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Vehicle mobility pattern-based handover scheme using discrete-time Markov chain

机译:基于离散时间马尔可夫链的基于车辆移动性模式的切换方案

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摘要

For the improvement of the quality of service (QoS) of wireless Internet users traveling in vehicles, it is effective to reduce the service disruption time by avoiding unnecessary handover occurrence, considering the vehicles' movement paths. This paper proposes a handover scheme suitable for users traveling in vehicles, which enables continuous learning of the handover process using a discrete-time Markov chain (DTMC). The proposed handover scheme avoids unnecessary handover trials when a short dwell time in a target cell is expected or when the target cell is an intermediate cell through which the vehicle quickly passes. For verifying the performance of the proposed scheme, we observe the average number of handover trials and the average throughput along various paths, which are real bus lines. The results show that the proposed scheme reduces the number of handover occurrences and maintains adequate throughput.
机译:为了提高在车辆中行驶的无线互联网用户的服务质量(QoS),考虑到车辆的移动路径,通过避免不必要的切换发生来减少服务中断时间是有效的。本文提出了一种适用于在车辆中行驶的用户的切换方案,该方案能够使用离散时间马尔可夫链(DTMC)连续学习切换过程。当预期在目标小区中的驻留时间较短时或当目标小区是车辆快速通过的中间小区时,提出的切换方案避免了不必要的切换试验。为了验证所提出方案的性能,我们观察了切换试验的平均次数和沿不同路径的平均吞吐量,这些路径是真实的总线。结果表明,该方案减少了切换次数,并保持了足够的吞吐量。

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