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Published in: Cluster Computing 5/2019

30-11-2017

RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

Authors: B. Sarala, D. Rukmani Devi, D. S. Bhargava

Published in: Cluster Computing | Special Issue 5/2019

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Abstract

Spectrum detecting is the essential and crucial mechanisms of cognitive radio (CR) to the invention the unemployed spectrum. CR system has been suggested as a conceivable resolution for enhancing the spectrum use by empowering unprincipled spectrum sharing. The principal prerequisite for enabling CR to utilize authorized range on an optional premise is not making interfering to primary users. The principal goal of CR is to use rare and limited natural resource efficiently with no obstruction to the primary users (PUs). This work presents an overview of CR architecture, discusses the characteristics and benefits of a CR. Energy identification, matched channel filter detection, and cyclostationary recognitions are most conventional techniques for spectrum sensing. The explanation behind picking energy detection procedure, it did not need any previous info from the primary user transmission. Additionally, the particular result of energy detection technique corrupts with a lower sign to noise ratio (SNR) level signal area. General detection performance of energy detection exceptionally depends upon noise, mainly while the SNR is low for PU. To consider this issue, this paper shows a remarkable, augmented threshold model for efficient energy detection procedure to improve the detection execution at low SNR level. The simulation results demonstrate the energy detection performance utilizing proposed system model is excellent than a fixed threshold at low SNR signal areas.

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Metadata
Title
RETRACTED ARTICLE: Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique
Authors
B. Sarala
D. Rukmani Devi
D. S. Bhargava
Publication date
30-11-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1311-8

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