HaolinLiu97 / deep_MIMO

Unofficial Pytorch implementation of Deep Learning-Based MIMO Communications (Timothy J. O’Shea)

deep_MIMO

Unofficial Pytorch implementation of Deep Learning-Based MIMO Communications (Timothy J. O’Shea)

Introduction

This is the course project of Liu Haolin for CIE 6014 in CUHKSZ.
This project is running on Python 3.6 for the deep learning MIMO, and is running on Matlab 2019b for the MMSE and SVD MIMO baseline.

And it only contains 2x2 spatial multiplexing MIMO.

install packages

Pytorch and some other packages are required, use the following commands to install the package:

pip install -r requirements.txt

run the deep learning training

run the training by typing the following command:

python train.py --exp_name=experiment_name --gpu=0 --batch_size=6 --num_bits=2

after the model is trained, the weight file will be stored under ./checkpoints/experiment_name/xxx.pth

test the trained model

I provide a trained model in ./checkpoints/deep_MIMO/epoch_99.pth, you can type the following command the test this model:

python test.py --resume=./checkpoints/deep_MIMO/epoch_99.pth --gpu=0 --batch_size=6

test your model

test the model by specifying the path of the model's weight

python test.py --resume=./checkpoints/experiment_name/xxx.pth --gpu=0 --batch_size=6

It will print the BER performance of the model under different SNR.

run the baseline model

the matlab codes for the baseline models is in "MIMO_baseline.m", you can use Matlab to directly run this file and obtain the BER performance of the baseline models.

some results

The BER performance among MMSE baseline, SVD baseline, and the deep learning based MIMO is shown in the following figure:
For SNR lower than 30dB, deep learning based MIMO outperforms the baseline. However, when SNR is higher than 30dB, two baseline models works better, and MMSE baseline is the best among them.