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Multi-dimensional K-factor analysis for V2V radio channels in open sub-urban street crossings

机译:开放式城郊街道交叉口V2V无线电信道的多维K因子分析

摘要

In this paper we analyze the small-scale fading statistics for vehicle-to-vehicle (V2V) communications in a typical open sub-urban street crossing. The two cars approach the crossing from two different streets and the channel conditions vary from non line-of sight (NLOS) to line-of-sight (LOS). The small-scale fading of the first delay bin is Ricean distributed with a time-varying K-factor. The later delay bins are mostly Rayleigh distributed. The antenna arrays used for recording the multiple-input multiple-output channels are linear and consist of 4 elements with directional radiation patterns. We investigate the K-factor variation of the first delay bin in time, frequency, and space dimensions, where the measurement has a duration of 20 s, a bandwidth of 240 MHz, and 16 individual single-input single- output channels. We observe that the large/small K-factor values are not necessarily correlated with the received power. We show that the K-factor can not be assumed to be constant in any of the considered domains, not even in the frequency domain, as it has been always done for relative bandwidths up to 10%. The narrow- band K-factor for each frequency bin corroborates the need to consider its frequency variation. The antenna radiation patterns, and the illuminated objects by them at different time instances are the cause of these variations. We conclude that a multi- dimensional varying K-factor models the large-scale statistical behaviour more accurately than a constant K-factor.
机译:在本文中,我们分析了典型的郊区开放街道交叉口的车对车(V2V)通信的小规模衰落统计。两辆车从两条不同的街道驶近十字路口,通道条件从非视线(NLOS)到视线(LOS)有所不同。第一延迟箱的小规模衰落是具有随时间变化的K因子的Ricean分布。后来的延迟箱大部分是瑞利分布的。用于记录多输入多输出通道的天线阵列是线性的,由4个具有定向辐射图的元件组成。我们研究了第一个延迟箱在时间,频率和空间维度上的K因子变化,其中测量持续时间为20 s,带宽为240 MHz,并且具有16个单独的单输入单输出通道。我们观察到大/小K因子值不一定与接收功率相关。我们表明,不能一直认为K因子在任何考虑的域中都是恒定的,即使在频域中也是如此,因为相对带宽高达10%的情况下总是如此。每个频点的窄带K系数证实了需要考虑其频率变化。这些变化的原因是天线辐射方向图以及它们在不同时间点所照亮的物体。我们得出结论,多维可变K因子比恒定K因子更准确地为大规模统计行为建模。

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