Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning

In this repository you can find the simulation source code of: "Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning".

Channel model

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"

Content

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

Dataset

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).

Requirements

1. torch 1.8.0 (Support Complex Tensor)
2. numpy 1.19.2

Copyright

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