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Short Overview of Cyclostationary Signal Processing.

PB97182448

Publication Date	1995
Personal Author	Feher, B.
Page Count	23
Abstract	The report demonstrates the efficiency of the new theory and presents important application areas where cyclostationarity signal processing is compulsory. This short overview presents only the basic definitions of the fraction-of-time probabilistic model, the second-order cyclic autocorrelation function and the spectral correlation density function. The effect of the linear time-invariant transformations and periodic time sampling operations on the cyclostationary signals is outlined. Among the application examples, signal detection, parameter estimation of communication signals and possible exploitation of cyclic features in synchronization problems are discussed. The noise immunity of the cyclic system identification algorithms is argued and different methods are presented using the cyclic spectral correlation properties. For blind equalization of digital communication channels, both the frequency and the time domain algorithms are described. Finally, the basic properties of polyperiodic time-variant filtering are presented.
Keywords	Signal processing Periodic variation Digital communications Signal detection Spectral correlation Synchronization(Electronics) Electric filters Temporal variations Time domain Frequency domain Autocorrelation Algorithms Foreign technology Cyclostationary signals Second-order periodicity Fraction-of-time probability
Source Agency	Netherlands Participating Organizations
Corporate Authors	Technische Univ. Delft (Netherlands). Dept. of Applied Physics.
Supplemental Notes	Details in illustrations may not be fully legible in microfiche.
Document Type	Technical Report
NTIS Issue Number	199721

Short Overview of Cyclostationary Signal Processing.

Short Overview of Cyclostationary Signal Processing.
PB97182448

Publication Date	1995
Personal Author	Feher, B.
Page Count	23
Abstract	The report demonstrates the efficiency of the new theory and presents important application areas where cyclostationarity signal processing is compulsory. This short overview presents only the basic definitions of the fraction-of-time probabilistic model, the second-order cyclic autocorrelation function and the spectral correlation density function. The effect of the linear time-invariant transformations and periodic time sampling operations on the cyclostationary signals is outlined. Among the application examples, signal detection, parameter estimation of communication signals and possible exploitation of cyclic features in synchronization problems are discussed. The noise immunity of the cyclic system identification algorithms is argued and different methods are presented using the cyclic spectral correlation properties. For blind equalization of digital communication channels, both the frequency and the time domain algorithms are described. Finally, the basic properties of polyperiodic time-variant filtering are presented.
Keywords	Signal processing Periodic variation Digital communications Signal detection Spectral correlation Synchronization(Electronics) Electric filters Temporal variations Time domain Frequency domain Autocorrelation Algorithms Foreign technology Cyclostationary signals Second-order periodicity Fraction-of-time probability
Source Agency	Netherlands Participating Organizations
Corporate Authors	Technische Univ. Delft (Netherlands). Dept. of Applied Physics.
Supplemental Notes	Details in illustrations may not be fully legible in microfiche.
Document Type	Technical Report
NTIS Issue Number	199721