Paper Info
Title
Improved Mcas Based Spectrum Sensing In Cognitive Radio
Abstract
This paper presents a computationally efficient cyclostationarity detection based spectrum sensing technique in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD), maximum cyclic autocorrelation selection (MCAS), and so on. PD can be affected by noise uncertainty because it requires a noise floor estimation, whereas MCAS does not require the estimation. Furthermore, the computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the estimation unnecessary whereas PD must compute only one statistic. In the presented MCAS based techniques, only one statistic must be computed. The presented technique obtains other necessary statistics from the procedure that computes the statistic. Therefore, the computational complexity of the presented is almost the same as that of PD, and it does not require the noise floor estimation for threshold. Numerical examples are shown to validate the effectiveness of the presented technique.
Year
DOI
Venue
2018
10.1587/transcom.2017EBP3134
IEICE TRANSACTIONS ON COMMUNICATIONS
Keywords
Field
DocType
cognitive radio network, spectrum sensing, cyclostationarity detection, maximum cyclic autocorrelation
Computer science,Computer network,Cognitive radio
Journal
Volume
Issue
ISSN
E101B
3
0916-8516
Citations 
PageRank 
References 
1
0.36
0
Authors
1
Name
Order
Citations
PageRank
Shusuke Narieda1199.15