ESTIMATION OF NOISE IMMUNITY OF COMMUNICATIONCHANNELS WITH FADING WHEN INTEGRATED NEURAL NETWORK
The article considers methods of assessment of noise immunity in the communication channels with the integration of a neural network. Noiseimmunity is estimated for the optimal receiver as the probability of erroneous signal reception. It is known that at the moment there are no effective methods to combat multiplicativeinterference. The standard scheme of a communication channel with additive Gaussian noise against thebackground of multiplicative distortions of a binary discrete communication channel is considered. As amultiplicative interference, consider intersymbol interference. At the same time, a neural network isintegrated at the level of the first decision scheme. A neural network is a selflearning element of amatched filter. This integration allows you to take into account the current distortion of the receivedsignal. This allows you to organize the reception of the signal "as a whole" and increase the signal/noiseratio at the output of the first decider. Obtaining exact analytical expression depends on the type ofintegrated network, method of training, its layering, etc. The paper considers a simple scheme of thenetwork of direct distribution with training "without a teacher" according to the algorithm of Hebba.Numerical calculations are carried out. The proposed scheme is valid for slow fading when the period ofdistortion exceeds the duration of the elementary symbol. It is also effective for periodic fading. Theresults can be investigated in more complex schematic solutions.