Simulation code for "Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning" by Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau, 2022.

Simulation for "On the total energy efficiency of cell-free massive MIMO" by H. Q. Ngo, L.-N. Tran, T. Q. Duong, M. Matthaiou, E. G. Larsson, IEEE Trans. Green Commun. and Network., vol. 2, no. 1, pp. 25-39, Mar. 2018.

Submitted paperwork

Allows to reproduce the figures and the appendix of the paper "Correctly Modeling TX and RX Chain in (Distributed) Massive MIMO - New Fundamental Insights on Coherency"

Official repository for ASSENT: Learning-Based Association Optimization for Distributed Cell-Free ISAC — MILP formulation, GNN framework, and dataset.

Using the Unsupervised NN (UnsupNN) to solve the vast majority of parametric optimizations

Design of experiments (DoE) and machine learning packages for the iCFree project

Detect somatic SNVs from deep targeted UMI-seq cfDNA

Long term study on cell-free massive MIMO for 6G communication.

PyTorch implementation of Uncertainty-Aware Rank-One MIMO Q Network for offline RL, with OOD uncertainty quantification and lower-bound opt. to robust policies 🐙