Deep Learning Propagation Models over Irregular Terrain

Mónica Ribero, R. Heath, H. Vikalo, D. Chizhik, R. Valenzuela
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引用次数: 14

Abstract

Accurate path gain models are critical for coverage prediction and radio frequency (RF) planning in wireless communications. In many settings irregular terrain induces blockages and scattering making it difficult to predict the path gain. Current solutions are either computationally expensive or slope-intercept fits that do not capture local deviations due to terrain variation, leading to large prediction errors. We propose to use machine learning to learn path gain based on terrain elevation as features. We implement different neural network architectures with dense and convolutional layers that could include effects difficult to describe with traditional models (e.g. back scatter). We test our framework on an extensive set of measured path gain data and consistently predict with 5 dB Root Mean Squared Error, an 8 dB improvement over traditional slope-intercept solutions.
不规则地形上的深度学习传播模型
准确的路径增益模型对无线通信中的覆盖预测和射频规划至关重要。在许多情况下,不规则的地形会引起阻塞和散射,使路径增益难以预测。目前的解决方案要么计算成本高,要么斜坡-截距拟合不能捕捉到由于地形变化而导致的局部偏差,从而导致很大的预测误差。我们建议使用机器学习来学习基于地形高程作为特征的路径增益。我们使用密集层和卷积层实现不同的神经网络架构,这些层可能包含难以用传统模型描述的效果(例如反向散射)。我们在一组广泛的测量路径增益数据上测试了我们的框架,并以5 dB的均方根误差一致地预测,比传统的斜率-截距解决方案提高了8 dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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