Supreme: Fine-grained Radio map Reconstruction via Spatial-Temporal Fusion Network

In this paper, we propose a fine-grained radio map reconstruction framework, called Supreme, based on crowd-sourced data in an image super-resolution manner.

Paper

Kehan Li, Jiming Chen, Baosheng Yu, Chao Li, Zhangchong Shen, Shibo He. "Supreme: Fine-grained Radio map Reconstruction via Spatial-Temporal Fusion Network.", Accept by IPSN 2020.

Requirements

Supreme uses the following dependencies:

Pytorch 0.4.3 and its dependencies
Numpy, Scipy and Pandas
CUDA 10.0 or latest version. And cuDNN is highly recommended

Model Training

Main arguments:

n_epochs：number of epochs of training
batch_size: training batch size
lr: learning rate
n_residuals: number of residual blocks
base_channels: number of feature maps
img__width: radio map width
img_height: radio map height
depth: number of historical radio maps
channels: number of radio map channels
sample_interval: interval of validation
zoom: upscale factor of radio maps
ext_flag: whether to use external factor

Examples on model training:

Training Supreme with default setting:

python train.py --ext_flag

Training Supreme with given setting (with external factor):

python train.py --n_residuals=16 --base_channels=64 --depth=6 --ext_flag

Training Supreme without external factor

python train.py --n_residuals=16 --base_channels=64 --depth=6

Model Test

To test trained model, following code can be used:

python test.py --n_residuals=16 --base_channels=64 --depth=6

Dataset