mbaqer/V2X-mmWave-Beamforming
PyTorch implementation of multi-modality sensing in 60 GHz mmWave beamforming for connected vehicles.
This project helps researchers and engineers in connected vehicle communication design. It provides a PyTorch implementation for optimizing 60 GHz mmWave beamforming, which is crucial for reliable vehicle-to-vehicle (V2V) communication. By integrating various sensor data, it outputs more accurate beamforming decisions. This is ideal for those developing advanced communication systems for autonomous vehicles and smart transportation.
Use this if you are a telecommunications researcher or automotive engineer working on deep learning-based solutions for mmWave beamforming in V2V communication.
Not ideal if you are looking for a general-purpose simulation tool or a solution for lower frequency band communication systems.
Stars
9
Forks
1
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jan 14, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mbaqer/V2X-mmWave-Beamforming"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
NVlabs/sionna
Sionna: An Open-Source Library for Research on Communication Systems
lab-emi/OpenDPD
OpenDPD is an end-to-end learning framework built in PyTorch for power amplifier (PA) modeling...
utcsilab/score-based-channels
Source code for paper "MIMO Channel Estimation using Score-Based Generative Models", published...
DeepMIMO/DeepMIMO
DeepMIMOv4: A Toolchain and Database for Ray-tracing Datasets.
NVlabs/neural_rx
Real-Time Inference of 5G NR Multi-user MIMO Neural Receivers