Python code for "Deep Learning for Massive MIMO CSI Feedback"

(c) 2018 Wang-Ting Shih and Chao-Kai Wen e-mail: [email protected] and [email protected]

Introduction

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/

Requirements

• Python 3.5 (or 3.6)
• Keras (>=2.1.1)
• Tensorflow (>=1.4)
• Numpy

Steps to start

Step1. Download the Model

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.

Step2. Data Preparation

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

Step3. Run the file

Now, you are ready to run any *.py to get the results (i.e., CS-CsiNet and CsiNet in Table I of our paper).

Result

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