SSP-Raymobtime

Code and Data for the paper: Klautau, A., Oliveira, A., Pamplona, I. & Alves, W.. Generating MIMO Channels For 6G Virtual Worlds Using Ray-Tracing Simulations. IEEE Statistical Signal Processing Workshop, 2021.

Download the beam selection dataset from: https://nextcloud.lasseufpa.org/s/mrzEiQXE83YE3kg

Download the channel estimation dataset from: https://nextcloud.lasseufpa.org/s/JdCJSYSWa3rKKAQ

Python dependencies

If you want to use the already available preprocessed data that we make available, to train and test this baseline model the only dependencies are:

You may install these packages using pip or similar software. For example, with pip:

pip install tensorflow

Training and testing for Beam Selection

After download the data and save it at SSP_data/bs_baseline_data/, run the following command in the beam_selection directory:

python beam_selection.py

Training and testing for Channel Estimation

After download the data and save it at SSP_data/ce_baseline_data/, run the following command in the channel_estimation directory:

python train.py mimo_fixed

Parameters
- (Required) --model_name for channel estimation simulation, it represents model name and channel data that will be used to train or test.
- (Optional) --plots plot the accuracy and validation accuracy of your model.

Also, you should create models and results folders inside channel_estimation directory.

To test a channel estimation model, use test.py file:

python test.py mimo_fixed

Citation

@inproceedings{
    klautau2021,
    title={Generating {MIMO} Channels for {6G} Virtual Worlds Using Ray-tracing Simulations},
    author={Aldebaro Klautau and Ailton De Oliveira and Isabela Trindade and Wesin Alves},
    booktitle={IEEE Statistical Signal Processing Workshop},
    year={2021},
    month={Jul}
}

lasseufpa/SSP-Raymobtime

SSP-Raymobtime

Python dependencies

Training and testing for Beam Selection

Training and testing for Channel Estimation

Citation