This repository contains the code from the work detailed in the paper submitted to IEEE Access
@article{Thrane2020,
author = {Jakob Thrane, Darko Zibar, Henrik L. Christiansen},
title = {{Model-aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz}},
month = Jan,
year = 2020,
publisher = {IEEE},
journal = {IEEE Access}
}
Previous work is detailed in:
@article{Thrane2018,
author = {Thrane, Jakob and Artuso, Matteo and Zibar, Darko and Christiansen, Henrik L},
journal = {VTC 2018 Fall},
publisher = {IEEE}
title = {{Drive test minimization using Deep Learning with Bayesian approximation}},
year = {2018}
}
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Download the dataset from https://ieee-dataport.org/open-access/mobile-communication-system-measurements-and-satellite-images
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Put the raw data into the
raw_data
folder- such that the data is located in:
raw_data\feature_matrix.csv
raw_data\output_matrix.csv
raw_data\mapbox_api\*.png
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Generate the test and training set using
generate_training_test.py
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Run the training of the model by
train.py
, see the script for commandline arguments
This directory formats SigCap data in exactly the same way as formatted in Thrane's paper, i.e.
,Longitude,Latitude,Speed,Distance,Distance_x,Distance_y,PCI,PCI_3,PCI_4,PCI_6,PCI_7,PCI_20,PCI_40,PCI_184,PCI_237
The SigCap data is collected using an Android phone.
The formatting of the last few columns (from the first PCI_i onwards to the right) depends on the PCI values available.
The exe file for geckodriver from https://github.com/mozilla/geckodriver/releases should be in venv/bin. Depending on the environment, geckodriver may need to be in another directory. Refer to https://github.com/thewati and https://medium.com/@watipasomulwafu for more information.
The data/data_{date} directory must be inside the formatted_data_gen directory.
Run getData_Michael.py to produce the csv file with formatted data. Run getImages_Michael.py to produce the satellite images based on the (latitude, longitude) pairs in the data/data_{date} file.
This directory produces height maps for data collected in DTU (Technical University of Denmark). The DSM_6188_720_2x2 directory mustw be present because it contains tiff files detailing the height information of DTU. grid_data stores the height maps and the feature_matrix which are generated using (latitude, longitude) values that are arranged in a grid.
grid_pattern_feature_gen.py generates the features in exactly the same format as in Thrane's paper based on gridded locations.
grid_pattern_height_map_gen.py generates the height maps in exactly the same format as Thrane's satellite images based on gridded locations.
heatmap_prototyping tests code for plotting a heat map of signal strength.
height_map_gen_orig_raw_data.py generates the height maps in exaclty the same format as Thrane's satellite images based on Thrane's (latitude, longitude) measurements provided in IEEE's data portal.
The directory raw_data must be present in the parent directory of DTU-processing-maps.