dumingyang20/CSSL-AMC-Pytorch
This is the official implementation for the paper: "A Contrastive Learner for Automatic Modulation Classification" (IEEE Trans. Wireless Commun., vol. 24, no. 4, 2025).
This project helps wireless communication engineers classify different signal modulation types, even when signals are noisy. You input radio signal data, segmented by signal-to-noise ratio (SNR), and it outputs a trained model that can accurately identify modulation categories. It's designed for researchers and practitioners working with radio frequency data and signal processing.
No commits in the last 6 months.
Use this if you need to reliably identify various modulation schemes from radio signals, especially in environments with low signal-to-noise ratios.
Not ideal if you are working with non-radio frequency data or if your primary goal is not automatic signal classification.
Stars
12
Forks
—
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dumingyang20/CSSL-AMC-Pytorch"
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