FFT-based Identification of Gilbert-Elliott Data Loss Models

Nowadays, radio or Internet-based communication is gaining popularity in various fields, e.g., in signal processing. Due to the not reliable real-time communication, some of the data are lost during the transmission. There are several stochastic models allowing the analysis of this phenomenon. The spectral properties of these models result in specific distortion in the signals’ spectra. As the spectrum of the signal can easily be calculated via the fast Fourier transform (FFT), FFT-based identification methods of the data loss can be developed. In this paper a new identification method is proposed for the simpler cases of the Gilbert-Elliott model class. The paper summarizes the mathematical descripiton of data loss, and introduces the Gilbert-Elliott model family. The novelty of the paper is the identification method that is based on the autocorrelation function of the Gilbert-Elliott model. Practical results are presented on the validation of the identified model parameters, and the required number of FFT points. The theoretical results are supported by simulation and measurement examples.