WeSNet2019

Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling

Introduction:

This repository contains the codes for implementing the weight-scaled neural network design for building a low complexity learning-based multiple-inputs multiple-outputs MIMO receivers. The details can be found in our paper: [http://arxiv.org/abs/1909.06943]

Prerequisites

To run this code, you will need:

1. Python 3.6 or above
2. TensorFlow 1.15. You can however run the code in TensorFlow 2.xx by disabling the eager execution mode as follows:import tensorflow.compat.v1 as tf tf.disable_v2_behavior()

Datasets

The training and test datasets are generated stochastically from random normal distributions with different instantiations using either BPSK or QPSK modulation (see: data_generation.py).

WeSNet Model

The main model is contained in wesnet_model.py. Run wesent_model to compile the model.

Training and Testing

The train and test the model, run train_test.py