/large-scale-channel-prediction

Large-Scale Channel Prediction System for ICASSP 2023 Pathloss Radio Map Prediction Challenge (1st Rank!)

Primary LanguagePythonMIT LicenseMIT

Large-Scale Channel Prediction Systems

This repo is the official implementation of "Large-Scale Channel Prediction System for ICASSP 2023 Pathloss Radio Map Prediction Challenge".

Highlights

  • Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and 0.0383 on the challenge test set, placing it in the 1st-rank of the ML competition @ IEEE ICASSP 2023.
  • RMSE: 0.02569
map_USC

Citation

@inproceedings{lee2023large,
  author={Lee, Ju-Hyung and Lee, Joohan and Lee, Seon-Ho and Molisch, Andreas F.},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={PMNet: Large-Scale Channel Prediction System}, 
  year={2023},
}

Available checkpoints / dataset

Setup

Install packages using the following instruction.

pip install -r requirements.txt
  • Python 3.8.15
  • torch cuda

Evaluation

1. Run sh file

sh eval.sh

2. Run python file with arguments

python eval.py \
    --input_dir './RadioMap3DSeer/png' \
    --gt_dir './RadioMap3DSeer/gain' \
    --first_index 631 \
    --last_index 700 \
    --pretrained_model './checkpoints/radiomapseer3d_pmnetV3_V2_model_0.00076.pt' \
    --network_type 'pmnet_v3' \
    --output_dir './outputs/RadioMap3DSeer_Test'

The RMSE and run-time will be printed in results.txt file and the terminal where you run.

Arguments

  • input_dir : The directory where folders of input images such as antennasWHeight, buildingsWHeight exist. (It should be {your_directory}/png)

  • gt_dir : The directory where ground truth images exist. (It should be {your_directory}/gain)

  • first_index : The first index of map indices. For example, when we have 0_0.png ~ 83_79.png images, it should be 0 which is the first index of maps.

  • last_index : The last index of map indices. For example, in the above case, it should be 83 which is the last index of maps.

  • pretrained_model : The path of pretrained model.

  • network_type : The type of network. (pmnet_v3)

  • output_dir : The directory where predicted images are saved. (Default: {current_directory}/outputs)

  • Note: To use other datasets, one need to set arguments: input_dir, gt_dir, first_index, last_index