Simulation code for "Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming" by Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau, 2020.
-
Updated
Jul 7, 2023 - Python
#
deepmimo
Here are 6 public repositories matching this topic...
Codes for Channel State Information (CSI) prediction using deep learning.
benchmarking machine-learning deep-learning time-series pytorch dataset transformer ofdm wireless-communication 5g ofdm-wireless-communications massive-mimo mimo 6g mimo-ofdm deepmimo csi-prediction channel-state channel-es channel-pred
-
Updated
May 29, 2026 - Python
Analysis of 5G channel attenuation using the DeepMIMO ASU Campus 3.5 GHz ray-tracing scenario, comparing ray-traced path loss with the theoretical Friis free-space model under LoS and NLoS propagation conditions.
ray-tracing telecommunications wireless-communication 5g path-loss digital-twin 6g deepmimo free-space-path-loss friis-equation
-
Updated
Jul 10, 2026 - Jupyter Notebook
AI-powered 6G beam prediction using Deep Residual MLPs. Predicts optimal antenna beams from UE coordinates with 87.9% accuracy, eliminating exhaustive beam sweeping.
python machine-learning deep-learning signal-processing pytorch neural-networks telecommunications wireless-communications massive-mimo 6g deepmimo beam-prediction
-
Updated
Jun 11, 2026 - Jupyter Notebook
LoRA fine-tuning of the LWM wireless foundation model for 64-beam mmWave prediction across three DeepMIMO scenarios. Rank-4 adapters (4.82% of parameters) match full fine-tuning within 0.3 points on cross-scenario transfer.
transformers pytorch lora wireless-communications mmwave peft onnx deepmimo foundation-model beam-prediction
-
Updated
Sep 2, 2026 - Jupyter Notebook
Add this topic to your repo
To associate your repository with the deepmimo topic, visit your repo's landing page and select "manage topics."