Overview

This is the PyTorch implementation of paper "TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System". You can cite our paper by:

@ARTICLE{9705497,
  author={Cui, Yaodong and Guo, Aihuang and Song, Chunlin},
  journal={IEEE Wireless Communications Letters}, 
  title={TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System}, 
  year={2022},
  volume={11},
  number={5},
  pages={903-907},
  doi={10.1109/LWC.2022.3149416}}

or

Y. Cui, A. Guo and C. Song, "TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System," in IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 903-907, May 2022, doi: 10.1109/LWC.2022.3149416.

Requirements

We support a env.yaml in our project, so you can simply run

conda env create -f environment.yaml

to get a useable environment. Or manually install and build your own environment, to use this project, you need to ensure the following main requirements are installed.

• Python >= 3.7
• scipy
• 1.2 =< PyTorch <= 1.6
• thop==0.0.31-2005241907 Note that the latest version leads to bug.
• torchviz
• tensorboardX

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

You can check the performance of indoor and outdoor scenarios by downloading checkpoints in Google Drive. We support more detail checpoints in Google Drive. You can also check the authenticity of our results by training a new TransNet yourself and see its performance, the test NMSE and training MSE loss will be printed during your training. A 400 epochs training dosen't take very long (about 3 and half hours on a single RTX 2060), and you are able to reproduce TransNet-400ep results in Table 1 of our paper.

C. Project Tree Arrangement

We recommend you to arrange the project tree as follows.

home
├── TransNet  # The cloned TransNet repository
│   ├── dataset
│   ├── models
│   ├── utils
│   ├── main.py
├── COST2100  # The data folder
│   ├── DATA_Htestin.mat
│   ├── ...
├── Experiments
│   ├── checkpoints  # The checkpoints folder
│   │     ├── 4_in.pth
│   │     ├── ...
│   ├── run.sh  # The bash script
...

Train TransNet from Scratch

An example of run.sh is listed below. Simply use it with sh run.sh. It will start TransNet training from scratch. Change scenario by using --scenario . Change training epochs with '--epochs' and compression ratio with --cr.

python /home/TransNet/main.py \
  --data-dir '/home/COST2100' \
  --scenario 'in' \
  --epochs 400 \
  --batch-size 200 \
  --workers 0 \
  --cr 4 \
  --scheduler const \
  --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 TransNet with our 2 kind of training scheme (constant learning rate at 1e-4 for 400/1000 epochs).

Results of 400 epochs

Scenario	Compression Ratio	NMSE	Flops
indoor	1/4	-29.22	35.72M
indoor	1/8	-21.62	34.70M
indoor	1/16	-14.98	34.14M
indoor	1/32	-9.83	33.88M
indoor	1/64	-5.77	33.75M
outdoor	1/4	-13.99	35.72M
outdoor	1/8	-9.57	34.70M
outdoor	1/16	-6.90	34.14M
outdoor	1/32	-3.77	33.88M
outdoor	1/64	-2.20	33.75M

Results of 1000 epochs

Scenario	Compression Ratio	NMSE	Flops
indoor	1/4	-32.38	35.72M
indoor	1/8	-22.91	34.70M
indoor	1/16	-15.00	34.14M
indoor	1/32	-10.49	33.88M
indoor	1/64	-6.08	33.75M
outdoor	1/4	-14.86	35.72M
outdoor	1/8	-9.99	34.70M
outdoor	1/16	-7.82	34.14M
outdoor	1/32	-4.13	33.88M
outdoor	1/64	-2.62	33.75M

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

python /home/TransNet/main.py \
  --data-dir '/home/COST2100' \
  --scenario 'in' \
  --pretrained './checkpoints/4_in.pth' \
  --evaluate \
  --batch-size 200 \
  --workers 0 \
  --cr 4\ # Note that cr should be same as  checkpoints
  --cpu \
  2>&1 | tee test_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

Thanks two open source works, CRNet and CLNet, that build on work above and advance the CSI feedback problem in DL, you can find their related work in Github-Python-PyTorch CRNet and Github-Python-PyTorch CLNet

Thanks the Github project members for the open source Transformer tutorial, our base model for TransNet is based on their work.