Исследование нелинейной цифровой фильтрации сигналов с использованием генеративно-состязательной нейронной сети

The article presents the results of the study, as well as the structural schemes and parameters of the components of the generative-adversarial neural network. Graphical images of the results of filtering radio signals are given. Conclusions are drawn about the possibilities of using these neural networks. The purpose of the study. Substantiation of the possibilities of using generative-sensory artificial neural networks to solve problems of digital processing of radio signals. Materials and methods. To evaluate the results of digital filtering of noisy signals, the method of mathematical modeling in the Matlab environment was used. As test signals, the following were taken: a sine wave, a signal in the form of a sum of sinusoids, a model of a real radio-technical information signal. White Gaussian noise is used as the noise component. Also, filtering of the signal is carried out, in which there is no fragment of a certain length. A training sample was generated for the neural network of the generator, consisting of noisy test signals. A training sample of the discriminator neural network was also generated, consisting of test signals that do not contain noise. Results. Based on the simulation, it is concluded that the generative-adversarial neural network successfully solves the problems of isolating a useful signal in a mixture of it with noise of various physical nature. Such a neural network structure is also able to restore a useful signal if any part of it is missing as a result of external interference. Conclusion. The existing methods of digital filtering of radio signals require certain labor and time costs associated with the calculation of digital filters. Also, when designing high-order filters, it becomes difficult to calculate these filters. The idea of using a neural network in filtering tasks makes it possible to significantly reduce the filter design time, thus simplifying the process of its implementation. A neural network, which is a self-learning system, can find solutions that are inaccessible to conventional digital filtering algorithms. The results of this work can find their application in the field of digital signal processing and in the development of software-configurable radio.