Digital radio receiver based on neural network

Abstract

One of the areas of digital signal processing used in radio receivers and currently being investigated is the use of artificial neural networks. Signal reception is one of the most difficult theoretical and engineering problems in message transmission. The difficulty lies in the fact that messages need to be extracted from modulated signals, which are exposed to various distorting factors and interference in the radio channel. Therefore, it is desirable to have methods of reception that would be the best (optimal) in the given conditions. There are many different neural network topologies. Single–layer and multi-layer direct propagation are known – perceptrons, recurrent networks, self-organizing networks, as well as hybrid networks (radial-basis, hierarchical classifiers). Each of these types of topologies has its own advantages and disadvantages.The article analyzes current research and development in this area. The implementation of a radio receiver (demodulator) based on a multilayer perceptron is shown and the neural network is trained. Using National Instruments PXI equipment, a study was carried out, which showed that in comparison with an optimal receiver, the probability of a bit error in a receiver based on a neural network is higher, but insignificant. The operation of the receiver under the influence of harmonic interference showed that as the power of the interference increases, the probability of a BER error increases, and the closer the interference is to the carrier frequency, the higher the BER also becomes. Nevertheless, the neural network-based digital receiver under study remains operational at significant levels of interference that are close enough to the carrier frequency of the desired signal.