Approach to processing radio signals with amplitude modulation of many components using one-dimensional convolutional neural network

Abstract

The object of research is the methods of using one-dimensional convolutional neural networks in radio receiving systems in order to increase their interference resistance. The task of the research is to test the hypothesis about the likely higher efficiency of radio signal recognition under conditions of high noise (or weak signals) by neural network models of radio signal reception in comparison with trivial reception systems. With the use of one-dimensional convolutional neural networks, a higher efficiency of extracting useful information from a signal-noise mixture at sufficiently high noise levels and, accordingly, a higher accuracy of radio signal recognition accuracy has been achieved. This result was achieved due to the specific architecture of convolutional neural networks, the ability to automatically detect important patterns in the data and analyze radio signals more deeply and informatively. Hierarchical representation of data with the selection of more complex and abstract features of the signal as the convolutional neural models become more complicated is one of the main advantages of using the proposed methods and algorithms under complex conditions of radio signal transmission. The comparison with trivial methods of radio signal processing is performed on the basis of the symbol error probability parameter at different signal-to-noise ratios of the investigated signals and demonstrates a stable decrease in the symbol error probability at signal-to-noise ratios of less than 4 dB. The results could be used in real radio communication systems, especially under conditions where it is necessary to quickly and reliably recognize radio signals among noise, under conditions of interference or with weak signals. They could also be useful in military applications, Earth remote sensing systems, mobile communication networks, etc.