Cyclic spectrum power dencity estimation of info-communication signals

Radio engineering, including TV systems and devices


Аuthors

Efimov E. N.1*, Shevgunov T. Y.1**, Kuznetsov Y. V.2***

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. ,

*e-mail: omegatype@gmail.com
**e-mail: shevgunov@gmail.com
***e-mail: kuztetsov@mai-trt.ru

Abstract

The paper introduces cyclic periodogram averaging block algorithm for the estimation of cyclic power spectral density (CPSD) using time-smoothing approach. A brief overview of the cyclostationarity phenomena and the corresponding cyclic characteristic functions are provided; a detailed theoretical description of the proposed algorithm focusing on the task of cyclic power spectral density estimation of the finite length digital infocommunication signals is presented. The structure of the CPSD function on bispectral plane for the case of finite length digital signals is described. The properties of the support region of CPSD on bispectral plane such as resolution element shape and effective width are taken into the consideration in order to avoid significant gaps alongside cyclic frequency axis.

In order to demonstrate the proposed algorithm as well as the advantages of the cyclostationary approach itself, a numerical simulation is carried out. A mixture of two amplitude-modulated signals with wide-sense stationary random processes used as their modulation sequences is chosen for the simulation. The parameters of the simulation such as effective bandwidths of the mixture components and their carrying frequencies are selected in a manner that a significant overlapping in the frequency domain is to occur. The analysis of the estimated cyclic power spectral density as a two-variable function obtained with the proposed algorithm allowed to successfully determine the number of the components in the signal mixture, their carrier frequencies, separated periodograms for each of the components and make the conclusion of the statistical independency of the underlying random processes. The results of the numerical simulation confirm the correct work of the proposed algorithm as well as demonstrate the selective properties of the cyclostationary approach.

Keywords:

cyclostationarity, cyclic spectral power density, nonparametric estimation methods, periodograms, spectral correlation analysis

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