论文标题
使用随机顺序吸附过程对密集CSMA网络进行建模
Modeling of Dense CSMA Networks using Random Sequential Adsorption Process
论文作者
论文摘要
我们建模一个密集的无线局域网，其中访问点（APS）采用载体Sense多重访问（CSMA）型中式访问控制协议。在我们的模型中，使用随机顺序吸附（RSA）过程对活动AP集合的空间位置进行建模，这与MATéRN硬核点过程（MHPP-II）相比，该过程更准确，该过程更准确。利用统计物理学的RSA过程理论，我们为网络中典型AP的媒体访问概率提供了近似但准确的分析结果。此外，我们提出了一种数值方法来确定对相关函数$（\ mathtt {pcf}）$，这对于对干扰统计信息的准确估计非常有用。使用$ \ mathtt {pcf} $结果，我们得出网络中典型链接的信噪比interplus-plus-noise覆盖率的概率。我们通过广泛的蒙特卡洛模拟来验证理论结果的准确性。
We model a dense wireless local area network where the access points (APs) employ carrier sense multiple access (CSMA)-type medium access control protocol. In our model, the spatial locations of the set of active APs are modeled using the random sequential adsorption (RSA) process, which is more accurate in terms of the density of active APs compared to the Matérn hard-core point process of type-II (MHPP-II) commonly used for modeling CSMA networks. Leveraging the theory of the RSA process from the statistical physics literature, we provide an approximate but accurate analytical result for the medium access probability of the typical AP in the network. Further, we present a numerical approach to determine the pair correlation function $(\mathtt{PCF})$, which is useful for the accurate estimation of the interference statistics. Using the $\mathtt{PCF}$ result, we derive the signal-to-interference-plus-noise ratio coverage probability of the typical link in the network. We validate the accuracy of the theoretical results through extensive Monte Carlo simulations.
