Title :
Neural network surface acoustic wave RF signal processor for digital modulation recognition
Author :
Kavalov, Dimitar ; Kalinin, Victor
Author_Institution :
Sch. of Eng., Oxford Brookes Univ., UK
Abstract :
An architecture of a surface acoustic wave (SAW) processor based on an artificial neural network is proposed for an automatic recognition of different types of digital passband modulation. Three feed-forward networks are trained to recognize filtered and unfiltered binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals, as well as unfiltered BPSK, QPSK, and 16 quadrature amplitude (16QAM) signals. Performance of the processor in the presence of additive white Gaussian noise (AWGN) is simulated. The influence of second-order effects in SAW devices, phase, and amplitude errors on the performance of the processor also is studied.
Keywords :
AWGN; digital signals; feature extraction; feedforward neural nets; modulation; pattern classification; phase shift keying; quadrature amplitude modulation; quadrature phase shift keying; signal classification; surface acoustic wave filters; surface acoustic wave signal processing; 16QAM signals; ANN-based signal processor; AWGN; QPSK signals; SAW filter second-order effects; additive white Gaussian noise; amplitude errors; artificial neural network; automatic recognition; binary phase shift keying; digital modulation recognition; digital passband modulation; feedforward networks; filtered BPSK; neural network SAW RF signal processor; phase errors; processor performance simulation; quadrature amplitude modulation signals; quadrature phase shift keying; surface acoustic wave processor; unfiltered BPSK; AWGN; Acoustic waves; Artificial neural networks; Binary phase shift keying; Digital modulation; Feedforward systems; Neural networks; Passband; Quadrature phase shift keying; Surface acoustic waves;
Journal_Title :
Ultrasonics, Ferroelectrics, and Frequency Control, IEEE Transactions on
DOI :
10.1109/TUFFC.2002.1041545
