Using signal processing based features to train and validate machine-learning algorithms to improve spectrum sensing and related problems in cognitive radios.
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Updated
Feb 10, 2023 - Jupyter Notebook
Using signal processing based features to train and validate machine-learning algorithms to improve spectrum sensing and related problems in cognitive radios.
Spectrum sensing in cognitive radios leveraging machine learning models
In this repository, we deal with developing an energy detector and a detector based on cyclostationarity for an OFDM based cognitive radio system and implementing and evaluating the performance of these detectors.
Spectrum Sensing for Cognitive Radio
Exploring Rayleigh fading channels for NOMA users, our project uses Monte Carlo simulations to analyze signal detection across various SNRs.
Energy and polarization based interference mitigation
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