A.Yu. Parshin – Ph.D.(Eng.), Associate Professor, Department «Radio Engineering Devices»,  Ryazan State Radio Engineering University

E-mail: parshin.a.y@rsreu.ru

Yu.N. Parshin – Dr.Sc.(Eng.), Professor, Head of Department «Radio Engineering Devices»,  Ryazan State Radio Engineering University

E-mail: parshin.y.n@rsreu.ru

The article discusses the methods of signal processing in information transfer systems with ultra narrowband signals in relation to the tasks of implementing the systems of the Internet of Things (IoT) devices. The usage of ultra-narrowband signals is possible under the condition of a low transmission rate in the communication channel. In addition, this reduces the energy requirements for receiving and processing such signals. However, the usage of a small width of the signal spectrum leads to the problem of a high level of noise in the low frequency region.

Along with thermal noise, flicker noise has a noticeable effect. Their main feature is the type of power spectral density - inversely proportional to frequency. As a result, at low frequencies, the flicker noise power will be great compared to other types of noise. The fractional nature of the power spectral density makes it possible to use models of random processes with fractional dimensions to describe the properties of flicker noise. Thus, it becomes possible to estimate the parameters of such noise and their subsequent compensation. There are known and developed statistical methods for developing models of flicker noise, for example, the model of fractal Brownian motion with a fractional dimension, the measure of fractality of which is the Hurst exponent. Non-Gaussian models of fractal noise and interference are also being developed.

The article is devoted to the development of an optimal signal detection algorithm against the background of the sum of flicker noise and thermal noise. Linear filtering is performed for a Gaussian flicker noise model. For a non-Gaussian flicker noise, an algorithm is proposed based on the estimated-correlation-correlation approach. A model of a non-Gaussian process based on differential equations is considered. Equations are obtained for filter estimates of signal realizations. The noise immunity of the optimal signal processing against the background of fractal Brownian motion and thermal noise is analyzed for different values of the signal-noise ratio and the Hurst exponents of the noise models.

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