Name
Title | ||
|---|---|---|
Derivation of Sensing Features for Maximum Cyclic Autocorrelation Selection Based Signal Detection |
Abstract | ||
|---|---|---|
Maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing is one of the low complexity spectrum sensing techniques in cyclostationary detection techniques. However, spectrum sensing features of MCAS- based spectrum sensing have never been theoretically derived. This paper provides a derivation result of spectrum sensing characteristics for MCAS-based spectrum sensing in cognitive radio networks. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the examples demonstrate that numerical and theoretical values match well with each other. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VTCFall.2019.8891131 | 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) |
Keywords | Field | DocType |
signal detection probability,MCAS-based spectrum sensing,maximum cyclic autocorrelation selection-based spectrum sensing,cyclostationary detection techniques,sensing features,maximum cyclic autocorrelation selection based signal detection,low complexity spectrum sensing techniques,cognitive radio networks,false alarm probability | False alarm,Detection theory,Computer science,Electronic engineering,Cyclostationary process,Orthogonal frequency-division multiplexing,Autocorrelation,Cognitive radio | Conference |
ISSN | ISBN | Citations |
1090-3038 | 978-1-7281-1221-3 | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shusuke Narieda | 1 | 19 | 9.15 |
| Daiki Chot | 2 | 0 | 0.34 |
| Hiromichi Ogasawara | 3 | 0 | 0.34 |
| Kenta Umebayashi | 4 | 158 | 29.57 |
| takeo fujii | 5 | 248 | 76.17 |
| Hiroshi Naruse | 6 | 39 | 12.10 |