Paper Info
Title
Computational Complexity Reduction For Signal Cyclostationarity Detection Based Spectrum Sensing
Abstract
This paper presents a computationally efficient cyclostationarity detection based spectrum sensing in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD) or maximum cyclic autocorrelation selection (MCAS), and so on. Compared to both techniques, PD can be affected the noise uncertainty because PD requires the noise floor estimation whereas MCAS does not require the measurement. Furthermore, a computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the measurement unnecessary whereas PD must compute only one statistic. In the presented techniques that is based on MCAS, only one statistic must be computed unlike MCAS. Presented obtains other necessary statistics from the computation procedure of the statistic to be computed, and presented does not require the noise floor estimation as PD. Some numerical examples are shown to validate the effectiveness of the presented technique.
Year
Venue
Keywords
2017
2017 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
Cognitive radio network, spectrum sensing, cyclostationarity detection
Field
DocType
ISSN
Noise floor,Statistic,Detection theory,Computer science,Electronic engineering,Detector,Orthogonal frequency-division multiplexing,Computational complexity theory,Cognitive radio,Autocorrelation
Conference
0271-4302
Citations 
PageRank 
References 
0
0.34
8
Authors
1
Name
Order
Citations
PageRank
Shusuke Narieda1199.15