A-GCRNN: Attention Graph Convolution Recurrent Neural Network for Multi-band Spectrum Prediction

This is a PyTorch implementation of A-GCRNN in the following paper: A-GCRNN: Attention Graph Convolution Recurrent Neural Network for Multi-band Spectrum Prediction(https://ieeexplore.ieee.org/document/10251662). The dataset will be made publicly available after our next paper is accepted.

Structure

Dataset

The dataset for this project comes from the open source platform: https://electrosense.org

Dataset parameters	Value
Dataset source	https://electrosense.org
Sensor location	Madrid, Spain
Frequency band	500 MHz–800 MHz
Monitoring time	2021.5.28–2021.6.28
Frequency resolution	2 MHz
Time resolution	15 minutes
The dimensionality of samples	151 × 2880

！！！The opening time of sensors on this platform is uncertain, and there may be some sensors shutdown.

Usage

File description

Data: Datasets storage file
models: Models storage file
photo: Models and some experimental results image storage file
tasks: Tasks storage file
utils: Key function code storage file
adj_create.py: Code for constructing adjacency matrix
main.py: Training Code
tesy_main.py: Test Code

Requirements

Numpy
torch
pytorch-lightning
pandas
matplotlib

Model training

python main.py --model_name AGCRNN --max_epochs 3000 --learning_rate 0.0001 --batch_size 64 --hidden_dim 100  --settings supervised --gpus 1

Model test

python test_main.py --model_name AGCRNN --max_epochs 3000 --learning_rate 0.0001 --batch_size 64 --hidden_dim 100  --settings supervised --gpus 1

Parameters description

！！！These parameters can be adjusted independently.

Parameters	Description
--data	The name of the dataset
--seq_len	Required historical data length
--pre_len	Predicted data length
--split_ratio	Dataset spliting ratio
--hidden_dim	Number of GRU hidden layers

Run tensorboard --logdir lightning_logs/version_0 --samples_per_plugin scalars=999999999 in terminal to view the prediction results and experimental indicators.

Citation

Please cite the following paper if you use the code in your work:

@ARTICLE{ZhangTVT2023a,
  author={Zhang, Xile and Guo, Lantu and Ben, Cui and Peng, Yang and Wang, Yu and Shi, Shengnan and Lin, Yun and Gui, Guan},
  journal={IEEE Transactions on Vehicular Technology}, 
  title={A-GCRNN: Attention Graph Convolution Recurrent Neural Network for Multi-Band Spectrum Prediction}, 
  year={2023},
  doi={10.1109/TVT.2023.3315450}}

TLTLHILL / A-GCRNN-for-spectrum-prediction