A neural network model applied to the detection of digital signals

Author

Fernandes, Marcelo A C ; Neto, Adriao D D ; Bezerra, Joao B.

Author_Institution

Dept. de Eng. Eletr., Univ. Fed. do Rio Grande do Norte, Natal, Brazil

fYear

1998

fDate

9-13 Aug 1998

Firstpage

279

Abstract

This work presents an artificial neural network (ANN) approach for the signal decision problems associated with digital communication system receivers which use modulation schemes whose signal elements belong to finite bidimensional constellations. The decision system proposed, named a neural decoder (ND), is a multilayer perceptron neural network trained with a backpropagation algorithm and models a maximum-likelihood receiver. The ND training process and simulation results of their performance, regarding a conventional receiver, are presented for some of the modulation systems studied

Keywords

backpropagation; continuous phase modulation; decoding; digital communication; digital signals; frequency shift keying; multilayer perceptrons; phase shift keying; quadrature amplitude modulation; receivers; signal detection; CPFSK; PSK; QAM; artificial neural network; backpropagation algorithm; decision system; digital communication receivers; digital signal detection; finite bidimensional constellations; maximum-likelihood receiver; modulation schemes; multilayer perceptron neural network; neural decoder; neural network model; signal decision; signal elements; training process; Artificial neural networks; Constellation diagram; Digital communication; Digital modulation; Maximum likelihood decoding; Multi-layer neural network; Multilayer perceptrons; Neodymium; Neural networks; Signal detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications Symposium, 1998. ITS '98 Proceedings. SBT/IEEE International

Conference_Location

Sao Paulo

Print_ISBN

0-7803-5030-8

Type

conf

DOI

10.1109/ITS.1998.713132

Filename

713132