Application of a SAW artificial neural network processor to digital modulation recognition

Author

Kalinin, V. ; Kavalov, D.

Author_Institution

Sch. of Eng., Oxford Brookes Univ., Headington, UK

Volume

1

fYear

2000

fDate

36800

Firstpage

51

Abstract

The architecture of a SAW processor based on artificial neural network is proposed for automatic recognition of different types of digital passband modulation. Three feedforward networks are trained to recognize filtered and unfiltered BPSK and QPSK signals as well as unfiltered 16QAM signals. Performance of the processor in the presence of additive white Gaussian noise (AWGN) is simulated. The influences of second-order effects in SAW devices, phase and amplitude errors on the performance of the processor is studied

Keywords

AWGN; feedforward neural nets; phase shift keying; quadrature amplitude modulation; quadrature phase shift keying; surface acoustic wave signal processing; 16QAM; BPSK; QPSK; SAW artificial neural network processor; additive white Gaussian noise; amplitude errors; automatic recognition; digital modulation recognition; digital passband modulation; feedforward networks; filtered signals; phase errors; second-order effects; unfiltered signals; Artificial neural networks; Binary phase shift keying; Digital modulation; Frequency; Neural networks; Neurons; Quadrature phase shift keying; SAW filters; Surface acoustic waves; Transversal filters;

fLanguage

English

Publisher

ieee

Conference_Titel

Ultrasonics Symposium, 2000 IEEE

Conference_Location

San Juan

ISSN

1051-0117

Print_ISBN

0-7803-6365-5

Type

conf

DOI

10.1109/ULTSYM.2000.922505

Filename

922505