Skip to content

Channel Reconstruction Network implemented in PyTorch

License

Notifications You must be signed in to change notification settings

Kylin9511/CRNet

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Overview

This is the PyTorch implementation of paper Multi-resolution CSI Feedback with deep learning in Massive MIMO System.

Requirements

To use this project, you need to ensure the following requirements are installed.

Project Preparation

A. Data Preparation

The channel state information (CSI) matrix is generated from COST2100 model. Chao-Kai Wen and Shi Jin group provides a pre-processed version of COST2100 dataset in Google Drive, which is easier to use for the CSI feedback task; You can also download it from Baidu Netdisk.

You can generate your own dataset according to the open source library of COST2100 as well. The details of data pre-processing can be found in our paper.

B. Checkpoints Downloading

The model checkpoints should be downloaded if you would like to reproduce our result. All the checkpoints files can be downloaded from Baidu Netdisk or Google Drive

C. Project Tree Arrangement

We recommend you to arrange the project tree as follows.

home
├── CRNet  # The cloned CRNet repository
│   ├── dataset
│   ├── models
│   ├── utils
│   ├── main.py
├── COST2100  # The data folder
│   ├── DATA_Htestin.mat
│   ├── ...
├── Experiments
│   ├── checkpoints  # The checkpoints folder
│   │     ├── in_04.pth
│   │     ├── ...
│   ├── run.sh  # The bash script
...

Train CRNet from Scratch

An example of run.sh is listed below. Simply use it with sh run.sh. It will start advanced scheme aided CRNet training from scratch. Change scenario using --scenario and change compression ratio with --cr.

python /home/CRNet/main.py \
  --data-dir '/home/COST2100' \
  --scenario 'in' \
  --epochs 2500 \
  --batch-size 200 \
  --workers 0 \
  --cr 4 \
  --scheduler cosine \
  --gpu 0 \
  2>&1 | tee log.out

Results and Reproduction

The main results reported in our paper are presented as follows. All the listed results can be found in Table1 of our paper. They are achieved from training CRNet with our advanced training scheme (cosine annealing scheduler with warm up for 2500 epochs).

Scenario Compression Ratio NMSE Flops Checkpoints
indoor 1/4 -26.99 5.12M in_04.pth
indoor 1/8 -16.01 4.07M in_08.pth
indoor 1/16 -11.35 3.55M in_16.pth
indoor 1/32 -8.93 3.28M in_32.pth
indoor 1/64 -6.49 3.16M in_64.pth
outdoor 1/4 -12.70 5.12M out_04.pth
outdoor 1/8 -8.04 4.07M out_08.pth
outdoor 1/16 -5.44 3.55M out_16.pth
outdoor 1/32 -3.51 3.28M out_32.pth
outdoor 1/64 -2.22 3.16M out_64.pth

As aforementioned, we provide model checkpoints for all the results. Our code library supports easy inference.

To reproduce all these results, simple add --evaluate to run.sh and pick the corresponding pre-trained model with --pretrained. An example is shown as follows.

python /home/CRNet/main.py \
  --data-dir '/home/COST2100' \
  --scenario 'in' \
  --pretrained './checkpoints/in_04' \
  --evaluate \
  --batch-size 200 \
  --workers 0 \
  --cr 4 \
  --cpu \
  2>&1 | tee log.out

Acknowledgment

Thank Chao-Kai Wen and Shi Jin group again for providing the pre-processed COST2100 dataset, you can find their related work named CsiNet in Github-Python_CsiNet

About

Channel Reconstruction Network implemented in PyTorch

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages