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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	International journal of dynamics and control Ročník 9; číslo 1; s. 299 - 307
Hlavní autoři:	Vadivukkarasi, S., Santhi, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!