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首页> 外文期刊>IEEE transactions on wireless communications >Interference Modeling for Cellular Networks Under Beamforming Transmission
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Interference Modeling for Cellular Networks Under Beamforming Transmission

机译：波束赋形传输下蜂窝网络的干扰建模

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摘要

We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mm-wave bands, respectively. Two candidate models are considered: the inverse Gaussian and the inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution to a reference one. The interference models are tested against data obtained by simulating a cellular network based on stochastic geometry. The results show that the three-parameter mixture model offers remarkably good fit to simulated interference power. The mixture model is further used to analyze the capacity of a cellular network employing joint transmit and receive beamforming and confirms a good fit with simulation.
机译：我们提出了在丰富和有限散射环境中采用MIMO波束成形的蜂窝系统中干扰功率分布的分析模型，该模型分别捕获了微波和毫米波波段中非视距信号的传播。考虑了两个候选模型：逆高斯和逆威布尔，都是两个参数的重尾分布。我们进一步提出将这两个分布的混合作为具有三个参数的模型。为了估计这些分布的参数，使用了三种方法：矩匹配，个体分布最大似然估计（MLE）和具有设计的期望最大化算法的混合分布MLE。然后，我们为混合模型参数引入简单的拟合函数，作为信道路径损耗指数和阴影方差的多项式。为了衡量这些模型的优劣，使用信息理论量度相对熵来捕获从模型分布到参考模型的距离。针对通过基于随机几何结构模拟蜂窝网络而获得的数据对干扰模型进行了测试。结果表明，三参数混合模型非常适合模拟干扰功率。混合模型还用于分析采用联合发送和接收波束成形的蜂窝网络的容量，并通过仿真确定了很好的拟合度。

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