Analysis of enhanced complex SVR interpolation and SCG-based neural networks for LTE downlink system. (2018)

• Record Type: Journal Article
• Authors: Charrada, Anis
• Is Part Of: International journal of intelligent engineering informatics. Volume 6:Number 3/4(2018)
• Page Start: 295
• Page End: 307
• Publication Date: 2018
• Subjects: support vector machine regression -- SVR -- scaled conjugate gradient back propagation -- SCG -- radial basis function -- RBF -- artificial neural network -- ANN -- OFDM -- long term evolution -- LTE; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

In this article, we operate and evaluate the performance of radial basis function (RBF)-based support vector machine regression (SVR) and scaled conjugate gradient back propagation (SCG)-based artificial neural network (ANN), to estimate the channel deviations in frequency domain using the standardised pilot symbols structure for LTE downlink system. We apply complex SVR and ANN to estimate the real vehicular a channel environment well-defined by the International Telecommunications Union (ITU). The suggested procedures use data obtained from the received pilot symbols to estimate the overall frequency response of the frequency selective multipath fading channel in two stages. In the first stage, each technique learns to adjust to the channel fluctuations, then, in the second stage, it predicts all the channel frequency responses. Lastly, in order to assess the abilities of the considered channel estimators, we deliver performance of complex SVR and ANN, which are compared to traditional least squares (LS) and decision feedback (DF) methods. Computer simulation results demonstrate that the complex RBF-based SVR approach has a better precision than other estimation methods.