DeepMIMO

DeepMIMO for Power Allocation

This is a MATLAB / Python code package modified from DeepMIMO to generate real channels for power allocation. 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] 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).

[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, 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.

Dataset Generation

To generate the dataset, please follow these steps:

Step 1: 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

Step 2: Run Matlab file generate_data_part1.m to generate To generate the DeepMIMO dataset based on this ray-tracing scenario

Step 3: Run Python file python3 generate_data_part2.py to generate labels labeld by WMMSE algorithm python3 generate_data_part2.py --o dataset_deepmimo_fastx3.pt --num_tasks 3 --num_train 20000-20000-20000