(c) 2018 Wang-Ting Shih and Chao-Kai Wen e-mail: [email protected] and [email protected]
This repository contains the original models described in Chao-Kai Wen, Wan-Ting Shih, and Shi Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8322184/
- Python 3.5 (or 3.6)
- Keras (>=2.1.1)
- Tensorflow (>=1.4)
- Numpy
There are two models in the paper:
- CsiNet: CSI sensing (or encoder) and recovery (or decoder) network
- CS-CsiNet: Only learns to recover CSI from CS random linear measurements
We provide two types of code:
- xxx_onlytest: This type of code is used to reproduce the results in our paper based on our training weights. The model and weights we trained are put in folder 'saved_model'.
- xxx_train: This type of code provide a procedure to train the weights yourself.
Download the data from https://drive.google.com/drive/folders/1_lAMLk_5k1Z8zJQlTr5NRnSD6ACaNRtj?usp=sharing. After you got the data, put the data as shown below.
*.py saved_model/ *.h5 *.json data/ *.mat Now, you are ready to run any *.py to get the results (i.e., CS-CsiNet and CsiNet in Table I of our paper).
The following results are reproduced from Table I of our paper:
| gamma | Methods | Indoor | Outdoor | ||
|---|---|---|---|---|---|
| NMSE | rho | NSME | rho | ||
| 1/4 | LASSO | -7.59 | 0.91 | -5.08 | 0.82 |
| BM3D-AMP | -4.33 | 0.8 | -1.33 | 0.52 | |
| TVAL3 | -14.87 | 0.97 | -6.9 | 0.88 | |
| CS-CsiNet | -11.82 | 0.96 | -6.69 | 0.87 | |
| CsiNet | -17.36 | 0.99 | -8.75 | 0.91 | |
| 1/16 | LASSO | -2.72 | 0.7 | -1.01 | 0.46 |
| BM3D-AMP | 0.26 | 0.16 | 0.55 | 0.11 | |
| TVAL3 | -2.61 | 0.66 | -0.43 | 0.45 | |
| CS-CsiNet | -6.09 | 0.87 | -2.51 | 0.66 | |
| CsiNet | -8.65 | 0.93 | -4.51 | 0.79 | |
| 1/32 | LASSO | -1.03 | 0.48 | -0.24 | 0.27 |
| BM3D-AMP | 24.72 | 0.04 | 22.66 | 0.04 | |
| TVAL3 | -0.27 | 0.33 | 0.46 | 0.28 | |
| CS-CsiNet | -4.67 | 0.83 | -0.52 | 0.37 | |
| CsiNet | -6.24 | 0.89 | -2.81 | 0.67 | |
| 1/64 | LASSO | -0.14 | 0.22 | -0.06 | 0.12 |
| BM3D-AMP | 0.22 | 0.04 | 25.45 | 0.03 | |
| TVAL3 | 0.63 | 0.11 | 0.76 | 0.19 | |
| CS-CsiNet | -2.46 | 0.68 | -0.22 | 0.28 | |
| CsiNet | -5.84 | 0.87 | -1.93 | 0.59 |