Development of signal processing and machine learning methods for spectrum sensing using autocorrelation features

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Publicado en:	SN Applied Sciences vol. 7, no. 11 (Nov 2025), p. 1237
Autor principal:	Sesham, Srinu
Otros Autores:	Suresh, Nalina, Chembe, Dickson Kanungwe
Publicado:	Springer Nature B.V.
Materias:	Wireless networks Accuracy Deep learning Internet of Things Hypothesis testing Regression analysis Communication 5G mobile communication Regression models Signal processing Frequency ranges Noise levels Machine learning Energy Spectrum allocation Autocorrelation Statistical analysis Correlation coefficients Correlation coefficient Learning algorithms Fourier transforms Sensors Support vector machines Methods Eigenvalues Performance degradation Complexity Signal to noise ratio Environmental
Acceso en línea:	Citation/Abstract Full Text Full Text - PDF
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