A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks

Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless st...

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Veröffentlicht in:	International journal of dynamics and control Jg. 9; H. 1; S. 299 - 307
Hauptverfasser:	Vadivukkarasi, S., Santhi, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021 Springer Nature B.V
Schlagworte:	Algorithms Bandwidths Bit error rate Classifiers Cognitive radio Collision dynamics Collision rates Complexity Control Control and Systems Theory Dynamical Systems Engineering Error detection False alarms Fast Fourier transformations Fourier transforms Frequencies Frequency spectrum Machine learning Narrowband Spectrum allocation State estimation Utilities Vibration Wireless communications Spectrum sensing Back propagation learning Ada Boost algorithms Supervised learning Cognitive radio networks Channel state estimation
ISSN:	2195-268X, 2195-2698
Online-Zugang:	Volltext
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