7abushahla/Spectrum-Sensing-QAT
Code and resources for the paper: "Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach"
This project helps wireless communication engineers optimize how devices detect available radio frequencies. It takes raw in-phase/quadrature (I/Q) radio signal data and outputs information about open 'spectrum holes,' which are unused frequencies. This is designed for engineers working on cognitive radio systems, particularly those deploying on small, battery-powered edge devices with limited processing power.
No commits in the last 6 months.
Use this if you are developing cognitive radio systems and need to implement highly efficient spectrum sensing on resource-constrained edge hardware, such as IoT devices or embedded systems.
Not ideal if you are looking for a general-purpose deep learning library for tasks unrelated to spectrum sensing, or if you are working with high-performance computing environments where resource constraints are not a primary concern.
Stars
7
Forks
—
Language
C
License
—
Category
Last pushed
Aug 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/7abushahla/Spectrum-Sensing-QAT"
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
utcsilab/score-based-channels
Source code for paper "MIMO Channel Estimation using Score-Based Generative Models", published...
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