Paper Info
Title
Signal Cyclostationarity Detection Based Spectrum Sensing Using Simple Diversity Combining Technique and Its Implementation.
Abstract
This paper presents a simple equal gain combining technique for cyclostationarity detection based spectrum sensing in cognitive radio networks. The presented technique is based on maximum cyclic autocorrelation function (MCAS) technique with a low computational cost relative to other cyclostationarity detection based spectrum sensing techniques. MCAS judges whether an orthogonal frequency division multiplexing signal is included in received signals, by comparing the peak and non-peak values of a cyclic autocorrelation function (CAF). In this paper, the signal-to-noise ratio (SNR) of the CAF, which is composed of the peak and non-peak values of the CAF, is employed. The presented technique attempts to improve the performance of spectrum sensing by combining CAFs obtained at each receive antenna and by obtaining a stable CAF SNR. Moreover, the presented technique is implemented on a software defined radio based testbed for the evaluation. The developed testbed is mainly composed of a Universal Software Radio Peripheral/GNU Radio which is one of the software defined radio receiver, and the spectrum sensing technique is experimentally demonstrated in an anechoic chamber using the 470–710 MHz frequency band allocated to ISDB-T (terrestrial digital broadcasting) in Japan. The effectiveness of the presented techniques is validated by these results.
Year
DOI
Venue
2017
10.1007/s11277-017-4210-7
Wireless Personal Communications
Keywords
Field
DocType
Cognitive radio network,Spectrum sensing,Signal cyclostationarity,OFDM signal,Diversity combining,Experiments
Telecommunications,Computer science,Software-defined radio,Frequency band,Universal Software Radio Peripheral,Electronic engineering,Real-time computing,Diversity combining,Anechoic chamber,Digital broadcasting,Orthogonal frequency-division multiplexing,Cognitive radio
Journal
Volume
Issue
ISSN
96
1
0929-6212
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Shusuke Narieda1199.15
Tetsuya Hada200.34