论文标题
在深入室内场景中经验NB-iot传播建模的实验评估
Experimental Evaluation of Empirical NB-IoT Propagation Modelling in a Deep-Indoor Scenario
论文作者
论文摘要
在深入室内场景中进行路径模型是一项艰巨的任务。一方面，仅取决于发射器接收器距离的理论公式太简单了。另一方面，发现影响特定情况下信号损失的所有重要因素通常是不可行的。在本文中，我们实验研究了深室特征的影响，例如室内深度，室内距离和距离最接近的隧道走廊的距离以及使用NB-iot对接收功率的影响。我们描述了在长长的地下隧道系统中执行的测量活动，并分析了涉及工程功能的线性回归模型。我们表明，在深层室内场景中，当前用于NB-IOT信号衰减的经验模型不准确。我们观察到1）室内距离和穿透深度不能很好地解释信号衰减，并使用现有模型将预测的误差增加2-12 dB； 2）确定了平均距离到最近走廊的有希望的特征。
Path-loss modelling in deep-indoor scenarios is a difficult task. On one hand, the theoretical formulae solely dependent on transmitter-receiver distance are too simple; on the other hand, discovering all significant factors affecting the loss of signal power in a given situation may often be infeasible. In this paper, we experimentally investigate the influence of deep-indoor features such as indoor depth, indoor distance and distance to the closest tunnel corridor and the effect on received power using NB-IoT. We describe a measurement campaign performed in a system of long underground tunnels, and we analyse linear regression models involving the engineered features. We show that the current empirical models for NB-IoT signal attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor distance and penetration depth do not explain the signal attenuation well and increase the error of the prediction by 2-12 dB using existing models, and 2) a promising feature of average distance to the nearest corridor is identified.
