Author_Institution :
Sch. of Electr. & Electron. Eng., Univ. of Manchester, Manchester, UK
Abstract :
Cognitive radio (CR) systems require awareness of spectrum occupancy in order to operate without causing harmful interference to primary users (PUs). Cyclostationary feature detection (CFD) is a preferred method for spectrum sensing under low signal-to-noise ratio (SNR) or/and noise uncertainty scenarios. To determine the presence, or otherwise, of PU signals, conventional CFD schemes tend to use test statistics over, either, multiple cycle frequencies for a fixed lag set, or, multiple lags for a fixed cycle frequency. This paper proposes a new method that jointly utilizes cycle frequencies and lags to produce more reliable test statistics. As the optimal way to apply this joint utilization requires prior knowledge of the 4th-order cyclic cumulant, which can be challenging to obtain, an alternative sub-optimal scheme independent of this cumulant knowledge will also be provided. It will be shown that, in the low SNR region, where it is most critical for CR applications, the proposed sub-optimal scheme can lead to similar detection performances as the optimal maximum likelihood technique. It will also be demonstrated that, compared to multi-cycle-frequency detection with selection combining, equal gain combining, or maximum ratio combining, the proposed provide superior performance.
Keywords :
cognitive radio; diversity reception; radio spectrum management; signal detection; 4th-order cyclic cumulant; CFD scheme; CR systems; PU signals; cognitive radio systems; cyclostationary feature detection; cyclostationary feature spectrum sensing; equal gain combining; fixed cycle frequency; fixed lag set; joint cycle frequency; lag utilization; maximum ratio combining; multicycle-frequency detection; noise uncertainty; optimal maximum likelihood technique; primary users; selection combining; signal-to-noise ratio; suboptimal scheme; test statistic reliability; Computational fluid dynamics; Correlation; Diversity reception; Feature extraction; Sensors; Signal to noise ratio; Statistical analysis; Cognitive radio; cyclostationary feature detection; fourth-order cumulant; spectrum sensing;