Supportive 5G Infrastructure Policies are Essential for Universal 6G (India5G)

This codebase enables 4G and 5G universal broadband strategies to be tested with the ultimate aim of helping to connect more people to a faster internet, as applied here for India.

If there are any additional queries or comments about the code, do not hesitate to reach out to me via email at 'eoughton [at] gmu [dot] edu' for further information.

Please cite the published paper associated with this codebase:

Citation

• Oughton, E.J., and Jha, A.. (2021) Supportive 5G Infrastructure Policies are Essential for Universal 6G: Assessment Using an Open-Source Techno-Economic Simulation Model Utilizing Remote Sensing. IEEE Access 9, 101924–101945. https://doi.org/10.1109/ACCESS.2021.3097627.

Importantly, I provide all code (and required data inputs) so that the results can be reproduced. Both unit tests and integration tests are provided with the codebase to ensure reliability.

Method

A box diagram of the method is shown below, illustrating the open-source techno-economic 4G & 5G simulation model which takes advantage of remote sensing.

Example Results

A visualization of the cost per smartphone connected is shown below for 4G and 5G universal broadband strategies, demonstrating the type of results the codebase can produce.

Using conda

The recommended installation method is to use conda, which handles packages and virtual environments, along with the conda-forge channel which has a host of pre-built libraries and packages.

Create a conda environment called india5G:

conda create --name india5g python=3.6 gdal geopandas

Activate it (run this each time you switch projects):

conda activate india5g

Install india5G:

python setup.py install

Alternatively, for development purposes, clone this repo and run:

python setup.py develop

Download necessary data

You will need numerous input data sets. They are all detailed in the full IEEE paper, cited above. You will first want to download the Global Administrative Database (GADM), following the link below, making sure you download the "six separate layers":

• https://gadm.org/download_world.html

Place the data into the following path data/raw/gadm36_levels_shp.

Then download the WorldPop global settlement data from:

• https://www.worldpop.org/geodata/summary?id=24777.

Place the data in data/raw/settlement_layer.

Next, download the nightlight data here:

https://ngdc.noaa.gov/eog/data/web_data/v4composites/F182013.v4.tar

Place the unzipped data in data/raw/nightlights/2013.

Obtain the Mobile Coverage Explorer data from Collins Bartholomew:

https://www.collinsbartholomew.com/mobile-coverage-maps/mobile-coverage-explorer/

Place the data into data/raw/Mobile Coverage Explorer.

Preprocess necessary model inputs

You will need to run various scripts before using the model.

Firstly, run the following to preprocess all the supply and demand data:

python scripts/preprocess.py

Then you will need to generate the necessary lookup tables, using the provided cellular system simulation:

python scripts/sim.py

Using the model

To obtain model results once all inputs have been generated, simply execute the runner script:

python scripts/run.py

Using the R scripts in the india5G/vis folder, the results can be visualized, reproducing the graphics included in the IEEE paper.

Thanks for the support

India5G was written and developed at George Mason University.