ConvlstmCsiNet

Code for "Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback"

Introduction

This repo contains code accompaning the paper, "Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback", IEEE Wireless Communications Letters, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8951228

Dependencies

This code requires the following:

• Python 3.5 (or 3.6)
• TensorFlow v1.4+
• Keras v2.1.1+
• Numpy

Data

The data is available online:
https://drive.google.com/drive/folders/1_lAMLk_5k1Z8zJQlTr5NRnSD6ACaNRtj?usp=sharing.
After you got the data, create a folder named "data" and put the data files in it. Code files are outside the folder "data". Then run "data_transform.py" to generate the time-varying CSI according to the expression(3) in paper.

Model code

The four model code files stand for the four models mentioned in paper and named after with the corresponding model name, respectively. The code runs for the model training and is set to check the performance index(NMSE and cosine similarity) every several epochs to find the best model.

A few preparations should be taken before the training: Create a folder named "result" to save the models and create the subfolders corresponding with each model in each situation:

CSI/

.py
result/

ConvlstmCsiNet_A_indoor/
ConvlstmCsiNet_B_outdoor/

dim_256/
dim_512/

.h5
.json
.png

Contact

To ask questions or report issues, please contact: Y. Li and H. Wu(Email:xiangyi li@tju.edu.cn; whming@tju.edu.cn)