NDLI: Machine learning-based models for spectrum sensing in cooperative radio networks

Content Provider	IET Digital Library
Author	Tavares, Caio Henrique Azolini Marinello, Jose Carlos Proenca, Mario Lemes Abrao, Taufik
Abstract	In this study, the authors consider the application of machine learning (ML) models in cooperative spectrum sensing of cognitive radio networks (CRNs). Based on a statistical analysis of the classic energy detection scheme, the probability of detection and false alarm is derived, which depends solely on the number of samples and signal-to-noise ratio of the secondary users. The channel occupancy detection obtained from the established analytical techniques such as maximum ratio combining and AND/OR rules is compared to different ML techniques, including multilayer perceptron (MLP), support vector machine, and Naive Bayes, based on receiver operating characteristic and area under the curve metrics. By using standard profiling tools, they obtain the computational performance of the analysed models during the training phase, a critical step for operating in CRNs. Ultimately, the results demonstrate that the MLP ML technique presents a better trade-off between training time and channel detection performance.
Starting Page	3102
Ending Page	3109
Page Count	8
ISSN	17518628
Volume Number	14
e-ISSN	17518636
Issue Number	Issue 18, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/14/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2019.0941
Journal	IET Communications
Publisher Date	2020-03-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Analysed Model Channel Detection Performance Channel Occupancy Detection Classic Energy Detection Scheme Cognitive Radio Cognitive Radio Network CRNs Different ML Technique Established Analytical Technique False Alarm Knowledge Engineering Technique Learning in AI Machine Learning Model Machine Learning-based Model Maximum Ratio MLP ML Technique Multilayer Perceptrons Neural Computing Technique Probability Radio Link And Equipment Radio Network Radio Spectrum Management Receiver Operating Characteristic Secondary User Signal Detection Signal to Noise Ratio Spectrum Sensing Statistical Analysis Statistics Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A review on energy based spectrum sensing in Cognitive Radio Networks

IMPROVED SNR BASED ADAPTIVE SPECTRUM SENSING FOR COGNITIVE RADIO NETWORKS

Analysis of energy detection based spectrum sensing over wireless fading channels in cognitive radio network.

Spectrum sensing with an improved Energy detection

In-band spectrum sensing in ieee 802.22 wrans for incumbent protection (2010)

Detection of spectrum hole from n-number of primary users using machine learning algorithms

Probabilities of detection and false alarm in multitaper based spectrum sensing for cognitive radio systems in AWGN

Kurtosis based spectrum sensing for cognitive wireless cloud computing network

Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems

Machine learning-based models for spectrum sensing in cooperative radio networks

Similar Documents

A review on energy based spectrum sensing in Cognitive Radio Networks

IMPROVED SNR BASED ADAPTIVE SPECTRUM SENSING FOR COGNITIVE RADIO NETWORKS

Analysis of energy detection based spectrum sensing over wireless fading channels in cognitive radio network.

Spectrum sensing with an improved Energy detection

In-band spectrum sensing in ieee 802.22 wrans for incumbent protection (2010)

Detection of spectrum hole from n-number of primary users using machine learning algorithms

Probabilities of detection and false alarm in multitaper based spectrum sensing for cognitive radio systems in AWGN

Kurtosis based spectrum sensing for cognitive wireless cloud computing network

Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems

Machine learning-based models for spectrum sensing in cooperative radio networks