In this repository you can find the simulation source code of: "Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning".
A realistic ray-tracing channel model is considered to evaluate the proposed solution. It has been introduced by Alkhateeb, et al, in "DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications"
1.DATASET.md: all parameters related to system model such as number of users, number of antennas, etc.
2.Codebook: designed codebook for each BS (4,5,8,9) in deepMIMO chanel model is available in the zip file.
3..py files: simulation source codes
The dataset where 4 APs with 64 antenna and 8 RF chains serving 4 single antenna users. We consider BS number 4,5,8,9 is active and other information is in the paper. The dataset name should be "dataSet_130.npy". The RSSI value must be normalized and the order of data in .npy can be found in codes (import .npy).
- torch 1.8.0 (Support Complex Tensor)
- numpy 1.19.2
Feel free to use this code as a starting point for your own research project. If you do, we kindly ask that you cite the following paper: "Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning".
@ARTICLE{9729183,
author={Hojatian, Hamed and Nadal, Jérémy and Frigon, Jean-François and Leduc-Primeau, François},
journal={IEEE Communications Letters},
title={Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/LCOMM.2022.3157161}}
Copyright (C): GNU General Public License v3.0 or later