Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
Foundations of the functional approach for signal analysis
Signal Processing - Special section: Multimodal human-computer interfaces
EURASIP Journal on Advances in Signal Processing - Special issue on dynamic spectrum access for wireless networking
Wireless Personal Communications: An International Journal
Cyclostationarity-based blind classification of analog and digital modulations
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Expert Systems with Applications: An International Journal
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Robust automatic modulation classification and blind equalization: novel cognitive receivers
Analog Integrated Circuits and Signal Processing
Wireless Personal Communications: An International Journal
Hi-index | 0.00 |
Traditional methods of signal classification, including phase and frequency histograms, modulus measurements, and power-spectrum measurements, fail when the signal-to-noise ratio is sufficiently low or when there are interfering signals present. These methods fail because the interfering signals and noise contribute substantially to the measured values of the classification features, thereby obscuring the contribution to the measurement from the signal of interest. The required signal selectivity of classification features for this situation can, in some instances be provided by features based on the cyclostationarity of both the signal of interest and the interferers. A set of cyclic-cumulant-based features for signal classification is proposed and analyzed, and results of classification experiments using simulated data are presented. The simulation results reveal that each of a number of spectrally overlapping signals can be successfully classified by measuring and processing the proposed features.