Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

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International Journal of Communications, Network and System Sciences

ISSN Print: 1913-3715
ISSN Online: 1913-3723
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"Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio"
written by Sandhya Pattanayak, Palanaindavar Venkateswaran, Rabindranath Nandi,
published by International Journal of Communications, Network and System Sciences, Vol.6 No.3, 2013

has been cited by the following article(s):

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[1]	Detección de espectro en banda ancha Sub-Nyquist para redes Radio Cognitiva: compleción de matrices mediante valores semilla
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[2]	Sub-Nyquist Wideband Spectrum sensing for Cognitive Radio Networks: Matrix Completion via seed values
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[3]	Cooperative spectrum sensing optimization based adaptive neuro-fuzzy inference system (ANFIS) in cognitive radio networks
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[4]	Cooperative spectrum sensing optimization based adaptive neuro‑fuzzy inference system (ANFIS) in cognitive radio networks
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[5]	A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network
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[6]	Predicting Spectral Opportunities in Cognitive Radio Network based on Neuro-Fuzzy for Bandwidth Optimization
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[7]	An optimised neural network-based spectrum prediction scheme for cognitive radio
	2019

[8]	Survey of Artificial Intelligence Approaches in Cognitive Radio Networks
	2019

[9]	Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks
	Wireless Personal Communications, 2018

[10]	Probability Density Function Estimation in OFDM Transmitter and Receiver in Radio Cognitive Networks based on Recurrent Neural Network
	2018

[11]	Modeling and Prediction Primary Nodes in Wireless Networks of Cognitive Radio Using Recurrent Neural Networks
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[12]	Machine Learning Applied to an RF Communication Channel
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[13]	Implementation of a machine learning based modulation scheme in GNURadio for over-the-air packet communications
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[14]	Implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva
	2017

[15]	From Sensing to Predictions and Database Technique
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[16]	From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
	Wireless Personal Communications, 2017

[17]	Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning
	Artificial Intelligence Review, 2017

[18]	Estimation of Future Occupation of Spectral Channels by Licensed Users in Cognitive Radio Networks Applying Neuro-Fuzzy Models
	2017

[19]	Soft Decision based Spectrum Sensing for Cognitive Radio Networks
	International Journal of Digital Application & Contemporary Research, 2015

[20]	Identification of spectrum holes using ANN model in TV bands with AWGN
	Wireless and Mobile, 2014 IEEE Asia Pacific Conference on, 2014

[21]	Spectrum hole detection in TV band using ANN model for opportunistic radio communication
	India Conference (INDICON), 2014 Annual IEEE, 2014

[1]	Cooperative spectrum sensing optimization based adaptive neuro-fuzzy inference system (ANFIS) in cognitive radio networks Journal of Ambient Intelligence and Humanized Computing, 2022 DOI:10.1007/s12652-020-02121-9

[2]	Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks Wireless Personal Communications, 2018 DOI:10.1007/s11277-017-5168-1

[3]	Machine Learning Applied to an RF Communication Channel NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018 DOI:10.1109/NAECON.2018.8556708

[4]	From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks Wireless Personal Communications, 2017 DOI:10.1007/s11277-017-4487-6

[5]	Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning Artificial Intelligence Review, 2017 DOI:10.1007/s10462-017-9600-4

[6]	Spectrum hole detection in TV band using ANN model for opportunistic radio communication 2014 Annual IEEE India Conference (INDICON), 2014 DOI:10.1109/INDICON.2014.7030479

[7]	Identification of spectrum holes using ANN model in TV bands with AWGN 2014 IEEE Asia Pacific Conference on Wireless and Mobile, 2014 DOI:10.1109/APWiMob.2014.6920284

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