Abstract
The problem of receiving and processing ultra-low-power signals of information transmission systems is being solved. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. A non-Gaussian flicker noise model is used based on a stochastic differential equation with a nonlinear drift coefficient. An optimal signal processing algorithm is being developed against the background of the sum of flicker noise and thermal noise based on an estimated-correlation-compensation approach. The analysis of the effectiveness of optimal signal processing against a background of non-Gaussian flicker noise and thermal noise is carried out.
Highlights
The problem of the frequency resource lack for data exchange is often encountered in modern information transmission systems
In [3], a statistical approach to the description of the properties of fractal objects and signals is described, and a model of the flicker noise (FN) in the form of a fractal Brownian motion is presented, which has a frequency dependence of the power spectral density on the FN
As a result of the analysis, the type of algorithm is determined in which the signalto-noise ratio (SNR) obtained as a result of processing monotonously increases with the growth of the analyzed sample volume
Summary
The problem of the frequency resource lack for data exchange is often encountered in modern information transmission systems. Due to the extremely small width of the information signal spectrum, the transition to processing on a zero carrier will lead to a sharp increase in the influence of lowfrequency noise on the processing results. In [3], a statistical approach to the description of the properties of fractal objects and signals is described, and a model of the flicker noise (FN) in the form of a fractal Brownian motion is presented, which has a frequency dependence of the power spectral density on the FN. FN studies in many fields of radio electronics [8] prove a nonlinear mechanism of FN formation and, as a result, its non-Gaussian probability distribution. The aim of this work is to analyze the processing efficiency of a narrow-band signal in the presence of a non-Gaussian FN for various types of algorithms: optimal, linear, nonlinear. As a result of the analysis, the type of algorithm is determined in which the signalto-noise ratio (SNR) obtained as a result of processing monotonously increases with the growth of the analyzed sample volume
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