ga-mmwave

This repository contains datasets, demo code, and results that complement academic material published in the IEEE Antennas and Wireless Propagation Letters.
Using these datasets requires appropriate citations.

S.B.F Gomes, N. Simmons, P. C. Sofotasios, M. D. Yacoub, and S. L. Cotton, "Genetic Algorithms for Channel Parameter Estimation in Indoor Millimeter-Wave Environments", published in the IEEE Antennas and Wireless Propagation Letters, 14 Sept. 2023.

Dependencies

This program is written in Python 3.9 and uses some dependencies listed in requirements.txt:

pip install -r requirements.txt

How to use

Just run the demo main file:

python main.py

It will create/use a folder called runs on the project root and plot the figures inside for each call of main.py.

To set the arguments, please go to arguments.py file.

This demo does not save the estimations anywhere.
To see the estimations for each algorithm, look at the command prompt.

Citation

If feel inspired, please consider cite:

  author={Gomes, Samuel Borges Ferreira and Simmons, Nidhi and Sofotasios, Paschalis C. and Yacoub, Michel Daoud and Cotton, Simon L.},
  journal={IEEE Antennas and Wireless Propagation Letters}, 
  title={Channel Parameter Estimation in Millimeter-Wave Propagation Environments Using Genetic Algorithm}, 
  year={2024},
  volume={23},
  number={1},
  pages={24-28},
  keywords={Fading channels;Genetic algorithms;Parameter estimation;Channel estimation;Sociology;Wireless communication;Probability density function;Channel measurements;genetic algorithms;meta-heuristic algorithms;millimeter-wave communications},
  doi={10.1109/LAWP.2023.3315422}}