A.Yu. Parshin - Ph.D.(Eng.), Associate Professor of Department of Radio Engineering Devices

Yu.N. Parshin - Dr.Sc. (Eng.), Professor, Head of Department of Radio Engineering Devices

Target setting. One of the features of devices and sensors of the IoT system is the need to ensure extremely high energy efficiency while maintaining the required reliability of information transmission. Moreover, many devices of the Internet of Things have low data transmission rate, which allows signal processing for a long time in a narrow frequency band. In this case, the signal spectrum is shifted to the high-intensity flicker noise region, which limits the possibility of improving the processing quality.

Aim of paper. Increasing the efficiency of signal processing against the background of flicker noise and thermal noise under conditions of a priori uncertainty of flicker noise parameters.

Results. The optimal algorithm for signal processing and filtering of flicker noise is obtained using a non-Gaussian flicker noise model in the form of a nonlinear stochastic equation. The evaluation-correlation-compensation processing algorithm contains the operation of nonlinear compensation of flicker noise. An adaptive Bayesian approach is used to eliminate a priori uncertainty of flicker noise parameters. The relationship between the parameters of the algorithm, setting the a priori uncertainty, and the measured parameters of flicker noise, is determined. Using the experimentally obtained samples of flicker noise, a statistical study of the obtained adaptive processing algorithm was carried out, its statistical characteristics were measured. A comparison is made of the resulting signal-to-noise ratio obtained by computer simulation and by experimental research depending on the sample size of the observed sum of signal, flicker noise and thermal noise.

Practical relevance. It was found that nonlinear signal processing against the background of flicker noise and thermal noise allows one to obtain a gain in the signal-to-noise ratio of more than 10 dB compared to linear processing. Thus, the power consumed by the transmitter from an autonomous power source and the battery life of the IoT device is reduced.

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