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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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
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Abstract	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.
AbstractList	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.
Author	Vadivukkarasi, S. Santhi, S.
Author_xml	– sequence: 1 givenname: S. surname: Vadivukkarasi fullname: Vadivukkarasi, S. email: vadivukkarasi.at2018@gmail.com organization: Department of Instrumentation Engineering, Annamalai University – sequence: 2 givenname: S. surname: Santhi fullname: Santhi, S. organization: Department of Instrumentation Engineering, Annamalai University
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Cites_doi	10.1109/SURV.2009.090109 10.5121/ijngn.2011.3203 10.1109/TWC.2008.060869 10.1109/T-WC.2008.070420 10.1109/JSAC.2014.140314 10.1016/j.jcss.2004.10.015 10.1155/2010/381465 10.1109/TVT.2010.2043968 10.1109/TVT.2013.2260834 10.1109/TCOMM.2013.050813.120877 10.1109/MWC.2013.6507399 10.1002/wcm.1017 10.1109/JSAC.2007.070409 10.1109/JSAC.2008.080103 10.1109/TWC.2009.081106 10.1109/ICC.2010.5502348
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SubjectTerms	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
Title	A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks
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