二项线Cox过程：统计特性表征及其在无线网络分析中的应用
Binomial Line Cox Processes: Statistical Characterization and Applications in Wireless Network Analysis
AI总结：
该研究针对街道部署的无线网络分析，提出更准确的二项线过程及对应二项线Cox过程，推导信干噪比元分布，分析时延与覆盖性能，为5G及未来网络分析提供支撑。
摘要：
当前针对收发信机受限分布于街道的无线网络的分析，大多基于泊松类模型，例如Poisson line processes（泊松线过程）和Poisson line Cox processes（泊松线Cox过程）。我们证明了在若干重要场景下，一种具有有限且确定数量街道的模型——被称为binomial line process（BLP，二项线过程）——具有更高的准确性。我们表征了BLP及对应的二项线Cox过程的统计特性，并将其应用于分析接入点沿城市街道部署的网络性能，这类部署场景将成为5G及未来无线网络的典型场景。为了细粒度地洞察网络性能，我们推导了信干噪比的meta distribution（元分布），并据此研究了平均本地传输时延与成功传输密度。这两项指标分别表征了网络的时延性能与覆盖性能，是下一代无线系统的关键性能指标。
英文摘要：
The current analysis of wireless networks whose transceivers are confined to streets is largely based on Poissonian models, such as Poisson line processes and Poisson line Cox processes. We demonstrate important scenarios where a model with a finite and deterministic number of streets, termed binomial line process, is more accurate. We characterize the statistical properties of the BLP and the corresponding binomial line Cox process and apply them to analyze the performance of a network whose access points are deployed along the streets of a city. Such a deployment scenario will be typical for 5G and future wireless networks. In order to obtain a fine-grained insight into the network performance, we derive the meta distribution of the signal-to-interference and noise ratio. Accordingly, we investigate the mean local delay in transmission and the density of successful transmission. These metrics, respectively, characterize the latency and coverage performance of the network and are key performance indicators of next-generation wireless systems.
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