Chaire C2M, LTCI, Telecom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France.
Sensors (Basel). 2023 Mar 29;23(7):3583. doi: 10.3390/s23073583.
With the increasing use of wireless communication systems, assessment of exposure to radio-frequency electromagnetic field (RF-EMF) has now become very important due to the rise of public risk perception. Since people spend more than 70% of their daily time in indoor environments, including home, office, and car, the efforts devoted to indoor RF-EMF exposure assessment has also increased. However, assessment of indoor exposure to RF-EMF using a deterministic approach is challenging and time consuming task as it is affected by uncertainties due to the complexity of the indoor environment and furniture structure, existence of multiple reflection, refraction, diffraction and scattering, temporal variability of exposure, and existence of many obstructions with unknown dielectric properties. Moreover, it is also affected by the existence of uncontrolled factors that can influence the indoor RF-EMF exposure such as the constant movement of people and random movement of furniture and doors as people are working in the building. In this study, a statistical approach is utilized to characterize and model the total indoor RF-EMF down-link (DL) exposure from all cellular bands on each floor over the length of a wing since the significance of distance is very low between any two points on each floor in a wing and the variation of RF-EMF DL exposure is mainly influenced by the local indoor environment. Measurements were conducted in three buildings that are located within a few hundred meters vicinity of two base station sites supporting several cellular technologies (2G, 3G, 4G, and 5G). We apply the one-sample Kolmogorov-Smirnov test on the measurement data, and we prove that the indoor RF-EMF DL exposure on each floor over the length of a wing is a random process governed by a Gaussian distribution. We validate this proposition using leave-one-out cross validation technique. Consequently, we conclude that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution and, therefore, can be characterized by the mean and the standard deviation parameters.
随着无线通信系统的广泛应用，由于公众风险感知的提高，对射频电磁场（RF-EMF）暴露的评估变得非常重要。由于人们每天超过 70%的时间都在室内环境中度过，包括家庭、办公室和汽车，因此对室内 RF-EMF 暴露评估的投入也在增加。然而，由于室内环境和家具结构的复杂性、多次反射、折射、衍射和散射的存在、暴露的时间可变性以及存在许多具有未知介电特性的障碍物，使用确定性方法评估室内 RF-EMF 暴露是一项具有挑战性和耗时的任务。此外，它还受到不受控制的因素的影响，这些因素会影响室内 RF-EMF 暴露，例如建筑物内人员的不断移动以及家具和门的随机移动。在这项研究中，利用统计方法来描述和建模从每个楼层的所有蜂窝频段的总室内 RF-EMF 下行链路（DL）暴露，因为在机翼的任何两个楼层之间，距离的重要性非常低，并且 RF-EMF DL 暴露的变化主要受局部室内环境的影响。在距离两个支持多个蜂窝技术（2G、3G、4G 和 5G）的基站站点几百米范围内的三栋建筑物中进行了测量。我们对测量数据应用了一个样本柯尔莫哥洛夫-斯米尔诺夫检验，证明了机翼长度内每层楼的室内 RF-EMF DL 暴露是由高斯分布控制的随机过程。我们使用留一交叉验证技术验证了这一命题。因此，我们得出结论，机翼长度内每层楼的室内 RF-EMF DL 暴露可以用高斯分布来建模，因此可以用均值和标准差参数来描述。
