Advanced Spectrum Sensing in Cognitive Radio Using Machine Learning & Deep Learning 🤖

research on spectrum sensing method of cognitive radio based on machine learning/deep learning algorithms

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The content of the project:
1. IEEE standard reference paper
2. PowerPoint presentation
3. Screenshots
4. Software
5. Source code
6. Videos
7. ReadMe document to guide.

ABSTRACT:
Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to
address the radio spectrum shortage. In particular, sensing of orthogonal frequency division multiplexing
(OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest.
Despite various efforts, most conventional OFDM sensing methods suffer from noise uncertainty, timing
delay and carrier frequency offset (CFO) that significantly degrade the sensing accuracy. To address
these challenges, this work develops two novel OFDM sensing frameworks drawing support from deep
learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method
(SAE-SS), in which a stacked autoencoder network is designed to extract the hidden features of OFDM
signals. Using these features to classify the OFDM user’s activities, SAE-SS is much more robust to noise
uncertainty, timing delay, and CFO than the conventional OFDM sensing methods.

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