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

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Wydane w:	SN Applied Sciences vol. 7, no. 11 (Nov 2025), p. 1237
1. autor:	Sesham, Srinu
Kolejni autorzy:	Suresh, Nalina, Chembe, Dickson Kanungwe
Wydane:	Springer Nature B.V.
Hasła przedmiotowe:	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
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