Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks
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Date
2022
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Journal Title
Journal ISSN
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Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Abstract
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.
Description
Keywords
Fixed Wireless Access, Satellites, Shadow mapping, Training, Images, Solid modeling, Deep learning, Wireless communication, Models, Receivers, Deep learning, Fixed Wireless Access, height map, Images, regression, Models, wireless channel parameter estimation
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Citation
7
WoS Q
Q2
Scopus Q
Q1
Source
Ieee Antennas and Wireless Propagation Letters
Volume
21
Issue
8
Start Page
1562
End Page
1566