- The official implementation of the VTC2021-Fall paper Machine Learning Based mmWave Orchestration for Edge Gaming QoE Enhancement.
- The repository uses the ViWi dataset.
- Python 3.6
- Pytorch 1.4
- Choose a scenario of the ViWi dataset.
- Generate the raw dataset by following the official instruction.
- Create a codebook.
- Use the raw dataset and the codebook to find the optimal beam index of each data point.
python preprocess.py \
--image_dir scenario/rgb/ \
--codebook data_generation_package/codebook \
--wireless data_generation_package/data/raw_data
- Generate the beam tracking dataset in csv format. Each data sample contains 13 consecutive beam indices.
python generate_dataset.py \
--beam_dir beam_dir/
Example:
Beam_1,Beam_2,Beam_3,Beam_4,Beam_5,Beam_6,Beam_7,Beam_8,Beam_9,Beam_10,Beam_11,Beam_12,Beam_13
114,114,113,113,113,113,113,112,112,112,112,112,112
- Split the dataset into training, validation, and testing sets.
python train.py \
--trn_data_path data/train_set.csv \
--val_data_path data/val_set.csv \
--store_model_path ckpt/
python inference.py \
--test_data_path data/test_set.csv \
--load_model_path ckpt/best
If you find our code helpful for your research, please consider citing the following paper:
@inproceedings{luo2021machine,
title={Machine Learning Based mmWave Orchestration for Edge Gaming QoE Enhancement},
author={Luo, Hao and Wei, Hung-Yu},
booktitle={2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},
pages={1--6},
year={2021},
organization={IEEE}
}