This is a MATLAB / Python code package modified from [1-2] to generate data and perform continual learning training [3-4] for coordinated beamforming. The code is based on the publicly available DeepMIMO dataset published for deep learning applications in mmWave and massive MIMO systems.
This MATLAB / Python code package is related to the following article:
[1] Alkhateeb, Ahmed, Sam Alex, Paul Varkey, Ying Li, Qi Qu, and Djordje Tujkovic. "Deep learning coordinated beamforming for highly-mobile millimeter wave systems." IEEE Access 6 (2018): 37328-37348.
[2] Ahmed Alkhateeb, “DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications,” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.
[3] Haoran Sun, Wenqiang Pu, Minghe Zhu, Xiao Fu, Tsung-Hui Chang, and Mingyi Hong, "Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment." arXiv preprint arXiv:2011.07782 (2020).
[4] Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu and Nikos D. Sidiropoulos, “Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management”, IEEE Transactions on Signal Processing 66.20 (2018): 5438-5453.
- pip install -r requirements.txt
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Download source data 'O1_60' under ‘O1’ Ray-Tracing Scenario from https://www.deepmimo.net/ray_tracing.html then put it into the folder: Data_Generation/RayTracing Scenarios/O1
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Run following scripts
matlab main_data_part1.m
python3 main_data_part2.py
sh main_train_part3.sh
matlab main_figure_part4.m