Application of a Neural Network for Estimating the Frequency Response of a Multipath Channel in Communication Systems with OFDM Technology
DOI:
https://doi.org/10.31649/1997-9266-2021-157-4-99-104Keywords:
OFDM, digital communication, wireless channel, multipath signal propagation, channel frequency response, channel estimation, neural networkAbstract
In the context of the development of modern technologies, the key role is assigned to the exchange of information. Digital communication systems are at the heart of information technology. To meet the needs of a modern person the requirements for communication systems are constantly increasing. Large information flows require high data rates. At the same time an important task is to reduce the number of errors that occur during data transmission. In OFDM systems, this is achieved by increasing the accuracy of estimating the frequency response of the communication channel. In this paper a neural network of direct propagation is used to estimate the values of the frequency response of the communication channel in OFDM systems. The neural network was designed for conditions when the pilot signals in the OFDM symbol structure are arranged in a combined pattern. Under such conditions the neural network receives noisy values of the frequency response on the pilot subcarriers as input information. Its task is to filter these values from noise and interpolate the values of the frequency response to the data subcarriers. The designed neural network has 32 incoming neurons, 128 outgoing neurons and 2 hidden layers of 8 neurons each. The structure of this neural network was designed with such an approach that the vector of the frequency response of the channel is estimated sequentially by 128 samples with their further combining. The neural network was trained on communication channels with given correlation properties by applying the error backpropagation method. The analysis of the efficiency of the network was carried out by means of statistical modeling using a model example in the Matlab computer-aided design system. The results of estimating the values of the frequency response using a neural network are compared with the results that are given by known methods. The analysis of the efficiency of the neural network showed that it is capable of providing a gain of up to 2 dB in comparison with the method of two-stage estimation of the frequency response estimation for a given model example. The neural network is inferior in the estimation accuracy to the minimum mean square error method, however, it has a lower implementation complexity compared to it.
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