Learning to optimize: Training deep neural networks for wireless resource management.
Python code to reproduce our works on DNN research for SPAWC 2017.
Demo.py contains the whole process from data generation, training, testing to plotting for 10 users' IC case, even though such process done on a small dataset of 25000 samples, 94% accuracy can still be easily attained in less than 100 iterations.
In test.py, we do the testing stage for Table I: Gaussian IC case in the paper, the testing are based on the pre-trained models. To train models from scratch, please follow the instructions in the paper and read the demo.py for reference.
All codes have been tested successfully on Python 3.6.0 with TensorFlow 1.0.0 and Numpy 1.12.0 support.
References: [1] Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu, and Nikos D. Sidiropoulos, "Learning to Optimize: Training Deep Neural Networks for Interference Management," in IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, 15 Oct.15, 2018.
version 1.0 -- February 2017.
Written by Haoran Sun and Xiangyi Chen (sun00111@umn.edu, chen5719@umn.edu)
June 2019. Add files to generate the IMAC model in the IMAC_model folder.
[2020 Update] Welcome to check out our recent work on "Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment"
paper available at https://arxiv.org/abs/2011.07782
code will be posted at https://github.com/Haoran-S/ICASSP2021
Setup
- Install python 3.6
- Install pycharm community edition
Running application
- Install pip dependencies
pip install -r requirements.txt
- Run python files
python3 demo.pypython3 test.py