Overview

This is a PyTorch implementation of BCRNet inference. The BCRNet network performance is improved with codeword mimic (CM) learning. The key results (BCRNet benchmark and BCRNet-CM) in paper Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback can be reproduced.

Requirements

The following requirements need to be installed.

• Python == 3.7
• PyTorch == 1.10.0

Project Preparation

A. Data Preparation

The channel state information (CSI) matrix is generated from COST2100 model and setting can be found in our paper. On the other hand, Chao-Kai Wen provides a pre-processed COST2100 dataset, which we adopt in BCsiNet training and inference. You can download it from Google Drive or Baidu Netdisk.

B. Checkpoints Downloading

The checkpoints of our proposed BCRNet can be downloaded from Baidu Netdisk (passwd: 5ipq) or Google Drive

C. Project Tree Arrangement

We recommend you to arrange the project tree as follows.

home
├── CodewordMimicFeedback  # The cloned current repository
│   ├── dataset
│   ├── models
│   ├── utils
│   ├── main.py
├── COST2100  # COST2100 dataset downloaded following section A
│   ├── DATA_Htestin.mat
│   ├── ...
├── Experiments
│   ├── checkpoints  # The checkpoints folder downloaded following section B
│   │     ├── in_cr4
│   │     ├── in_cr8
│   │     ├── ...
│   ├── run.sh  # The bash script
...

Key Results Reproduction

The key results reported in Table I of the paper are presented as follows.

Compression Ratio	Methods	Scenario	NMSE	Params	Checkpoints Path
1/4	BCRNet	indoor	-17.39dB	33K	in_cr4/bcrnet.pth
1/4	BCRNet-CM	indoor	-19.25dB	33K	in_cr4/bcrnet-cm.pth
1/4	BCRNet	outdoor	-8.90dB	33K	out_cr4/bcrnet.pth
1/4	BCRNet-CM	outdoor	-10.00dB	33K	out_cr4/bcrnet-cm.pth
1/8	BCRNet	indoor	-13.19dB	17K	in_cr8/bcrnet.pth
1/8	BCRNet-CM	indoor	-13.90dB	17K	in_cr8/bcrnet-cm.pth
1/8	BCRNet	outdoor	-6.31dB	17K	out_cr8/bcrnet.pth
1/8	BCRNet-CM	outdoor	-6.73dB	17K	out_cr8/bcrnet-cm.pth
1/16	BCRNet	indoor	-8.94dB	8K	in_cr16/bcrnet.pth
1/16	BCRNet-CM	indoor	-10.36dB	8K	in_cr16/bcrnet-cm.pth
1/16	BCRNet	outdoor	-4.36dB	8K	out_cr16/bcrnet.pth
1/16	BCRNet-CM	outdoor	-4.53dB	8K	out_cr16/bcrnet-cm.pth
1/32	BCRNet	indoor	-7.87dB	4K	in_cr32/bcrnet.pth
1/32	BCRNet-CM	indoor	-8.20dB	4K	in_cr32/bcrnet-cm.pth
1/32	BCRNet	outdoor	-2.91dB	4K	out_cr32/bcrnet.pth
1/32	BCRNet-CM	outdoor	-2.98dB	4K	out_cr32/bcrnet-cm.pth

The key results reported in Table II of the paper are presented as follows. Note that the performance of the original CsiNet can be found in their papers CsiNet and CsiNet+.

Compression Ratio	Methods	Scenario	NMSE	Params	Checkpoints Path
1/4	CsiNet-CM	indoor	-25.60dB	1.049M	in_cr4/csinet-cm.pth
1/4	CsiNet-CM	outdoor	-10.09dB	1.049M	out_cr4/csinet-cm.pth
1/8	CsiNet-CM	indoor	-15.33dB	0.525M	in_cr8/csinet-cm.pth
1/8	CsiNet-CM	outdoor	-7.63dB	0.525M	out_cr8/csinet-cm.pth
1/16	CsiNet-CM	indoor	-10.12dB	0.262M	in_cr16/csinet-cm.pth
1/16	CsiNet-CM	outdoor	-5.02dB	0.262M	out_cr16/csinet-cm.pth
1/32	CsiNet-CM	indoor	-8.75dB	0.131M	in_cr32/csinet-cm.pth
1/32	CsiNet-CM	outdoor	-3.38dB	0.131M	out_cr32/csinet-cm.pth

In order to reproduce the aforementioned key results, you need to download the given dataset and checkpoints. Moreover, you should arrange your project tree as instructed. An example of Experiments/run.sh can be found as follows.

python /home/CodewordMimicFeedback/main.py \
  --data-dir '/home/COST2100' \
  --scenario 'in' \
  --model 'bcrnet' \
  --pretrained './checkpoints/in_cr4/bcrnet.pth' \
  --batch-size 200 \
  --workers 0 \
  --reduction 4 \
  --cpu \
  2>&1 | tee log.out

Acknowledgment

This repository is modified from the BCsiNet open source code. Please refer to it for more information.

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.