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Bit error rate performance of Gaussian minimum frequency shift keying demodulation when BTb $\mathrm{B}{\mathrm{T}}_{b}$ = 0.5 and BTb $\mathrm{B}{\mathrm{T}}_{b}$ = 0.3.
Source publication
To solve the problem of bit error rate (BER) performance degradation over strong solar wind turbulence channel, this paper addresses to Gaussian minimum frequency shift keying (GMSK) demodulation using machine learning. First, by analyzing the scintillation characteristics of the telemetry signal caused by solar wind turbulence during the solar sup...
Citations
... Study [13] proposes the use of a one-dimensional convolutional network with recurrent elements of long short-term memory for signal processing with Gaussian two-position frequency manipulation under conditions of strong disturbances caused by the solar wind. ...
... Works [12,13] show a certain potential of using one-dimensional convolutional networks for digital signal processing. However, in these works, only the operation of the proposed models with such types of modulation, where there are only two symbols, is demonstrated. ...
... To validate the effectiveness of the improved mathematical model of the artificial neural network, it will be advisable to conduct testing on a large set of signal fragments. The same set of fragments was also preprocessed with a reference model, which is equivalent to a real receiving device working with AMMC [12,13]. The main evaluation criterion is the number of false identifications of symbols during signal processing. ...
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.
