An Application of Neural Networks to Channel Estimation of the ISDB-TB FBMC System

TL;DR

This paper explores the use of neural networks for channel estimation in the ISDB-TB digital TV system employing FBMC, aiming to improve signal reception quality amidst complex channel conditions.

Contribution

It introduces a neural network-based method for estimating the channel response in ISDB-TB, enhancing traditional techniques with AI-driven accuracy.

Findings

Neural network approach improves channel estimation accuracy.

Method effectively estimates channel response in ISDB-TB.

Potential for better reception of digital TV signals.

Abstract

Due to the evolution of technology and the diffusion of digital television, many researchers are studying more efficient transmission and reception methods. This fact occurs because of the demand of transmitting videos with better quality using new standards such 8K SUPER Hi-VISION. In this scenario, modulation techniques such as Filter Bank Multi Carrier, associated with advanced coding and synchronization methods, are being applied, aiming to achieve the desired data rate to support ultra-high definition videos. Simultaneously, it is also important to investigate ways of channel estimation that enable a better reception of the transmitted signal. This task is not always trivial, depending on the characteristics of the channel. Thus, the use of artificial intelligence can contribute to estimate the channel frequency response, from the transmitted pilots. A classical algorithm called Back-propagation Training can be applied to find the channel equalizer coefficients, making possible the correct reception of TV signals. Therefore, this work presents a method of channel estimation that uses neural network techniques to obtain the channel response in the Brazilian Digital System Television, called ISDB-TB, using Filter Bank Multi Carrier.