# meta-demodulator

**Repository Path**: zhoub86/meta-demodulator

## Basic Information

- **Project Name**: meta-demodulator
- **Description**: Code for the paper "Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-18
- **Last Updated**: 2021-03-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Meta-Demodulator

This repository contains code for "[Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://arxiv.org/abs/1908.09049)" - 
Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, and Joonhyuk Kang.

### Dependencies

This program is written in python 3.7 and uses PyTorch 1.1.0 and scipy.
Tensorboard for pytorch is used for visualization (e.g., https://pytorch.org/docs/stable/tensorboard.html).
- pip install tb-nightly, pip install future, and pip install scipy might be useful.

### Usage for offline scenario

- Train model:
    
    To train the demodulator with default settings, execute
    ```
    python main_offline.py
    ```
    For the default settings and other argument options, see top of `main_offline.py`
    
- Test model:
    
    To test the demodulator with default settings, execute
    ```
    python main_offline.py --path_for_meta_trained_net </path/to/saved-meta-model> --path_for_bm2_net </path/to/saved-joint-trained-model> 
    ```
    
-  In 'run' folder, basic examples (including experiments for the figures in the paper) can be found.
    
   In 'saved_nets' folder, trained models with most preferable settings in the paper (network for Fig. 6, 7, 9) can be found.
    
   In 'generated_data' folder, meta-training and meta-test set with 100 meta-devices for the realistic scenario can be found. 


### Usage for online scenario

- Run model:
    
    To train and test the demodulator in an online manner with default settings, execute
    ```
    python main_online.py
    ```
    For the default settings and other argument options, see top of `main_online.py`
    
    In 'run' folder, basic examples (including experiments for the figures in the paper) can be found.