In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter-receiver locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between transmitter and receiver. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, in a very accurate and computationally efficient manner. Our proposed method, termed RadioUNet, learns from a physical simulation dataset, and generates pathloss estimations that are very close to the simulations, but are much faster to compute for real-time applications. Moreover, we propose methods for transferring what was learned from simulations to real-life. Numerical results show that our method significantly outperforms previously proposed methods.

中文翻译：

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RadioUNet：使用卷积神经网络进行快速无线电地图估计

在本文中，我们提出了一种高效且非常准确的深度学习方法，用于估计点的传播路径损耗 $x$ （发射机位置）到任何点 $y$ 在平面域上。对于用户-小区站点关联和设备到设备链路调度等应用，准确了解所有发射机-接收机位置对的路径损耗函数非常重要。常用的统计模型将路径损耗近似为发射机和接收机之间距离的衰减函数。然而，在以建筑物、街道峡谷和不同高度物体为特征的现实传播环境中，这种径向对称函数会产生非常误导的结果。在本文中，我们展示了经过适当设计和训练的深度神经网络能够以非常准确和计算效率高的方式学习如何在给定城市环境的情况下估计路径损失函数。我们提出的方法，称为 RadioUNet，从物理模拟数据集中学习，并生成非常接近模拟的路径损耗估计，但对于实时应用程序的计算速度要快得多。此外，我们提出了将模拟中学到的知识转移到现实生活中的方法。数值结果表明，我们的方法明显优于先前提出的方法。