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

Uložené v:
Podrobná bibliografia
Vydané v:International journal of dynamics and control Ročník 9; číslo 1; s. 299 - 307
Hlavní autori: Vadivukkarasi, S., Santhi, S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Predmet:
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 prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2195-268X
2195-2698
DOI:10.1007/s40435-020-00633-y