RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks
By Ron Levie, cCaugkan Yapar, Gitta Kutyniok, Giuseppe Caire. IEEE Transactions on Wireless Communications, 2019
494 citations recorded by the source corpus.
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.
Subfields: Electrical and Electronic Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/8f05fef501b1e4cb4bd4f841c7f19f9274783143
How researchers see this paper
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In Electrical and Electronic Engineering
- Shuguang Cui
- Rui Zhang
- David Gesbert
- Emil Björnson
- Qingqing Wu
- Yong Zeng
- Merouane Debbah
- Robert Schober
In Signal Processing
Citations in the corpus
- A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, methodology): “On the other hand, if sufficient labelled data is available, deep neural networks such as RadioUNet can be trained to infer the channel path loss by using the transmitter location and morphological images of the urban geometry as the input of the neural network [68].”