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.
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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.
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Aug 16, 2025
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