utcsilab/score-based-channels
Source code for paper "MIMO Channel Estimation using Score-Based Generative Models", published in IEEE Transactions on Wireless Communications.
This project helps wireless communications researchers and engineers improve the accuracy of channel estimation in MIMO (Multiple-Input Multiple-Output) systems. It takes raw or pre-generated wireless channel data, specifically from Clustered Delay Line (CDL) models, and outputs a more precise estimation of the channel state, which is crucial for reliable wireless communication. This is for professionals working on advanced wireless communication technologies, such as 5G and beyond.
126 stars.
Use this if you are working on MIMO wireless communication systems and need to estimate channel conditions more accurately using modern machine learning techniques like diffusion models.
Not ideal if you are looking for a plug-and-play solution for basic channel estimation tasks without any programming or machine learning background.
Stars
126
Forks
29
Language
Python
License
—
Category
Last pushed
Jan 20, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/utcsilab/score-based-channels"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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...
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
abdulkarimgizzini/Temporal-Averaging-LSTM-based-Channel-Estimation-Scheme-for-IEEE-802.11p-Standard
This repository includes the source code of the LSTM-based channel estimators proposed in...