DocumentCode :
328071
Title :
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
Link To Document :
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