DocumentCode :
3488542
Title :
Classification of bandlimited FSK4 and FSK8 signals
Author :
Ramakonar, Vis ; Habibi, Daiyoush ; Bouzerdoum, Abdesselam
Author_Institution :
Sch. of Eng. & Math., Edith Cowan Univ., Joondalup, WA, Australia
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
398
Abstract :
This paper compares two types of classifiers applied to bandlimited FSK4 and FSK8 signals. The first classifier employs the decision-theoretic approach and the second classifier is a neural network structure. Key features are extracted using a zero crossing sampler. A novel decision tree is proposed and optimum threshold values are found for the decision theoretic approach. For the neural network, the optimum structure is found to be the smallest structure to give 100% overall success rate. The performance of the both classifiers has been evaluated by simulating bandlimited FSK4 and FSK8 signals corrupted by Gaussian noise. It is shown that the neural network outperforms the decision-theoretic approach particularly for SNR <10 dB
Keywords :
Gaussian noise; bandlimited signals; decision theory; feature extraction; frequency shift keying; neural nets; signal classification; FSK4 signals; FSK8 signals; Gaussian noise; bandlimited signals; decision tree; decision-theoretic approach; feature extraction; neural network structure; optimum threshold values; signal classification; zero crossing sampler; Australia; Decision trees; Digital modulation; Feature extraction; Frequency shift keying; Gaussian noise; Mathematics; Neural networks; Signal processing; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6703-0
Type :
conf
DOI :
10.1109/ISSPA.2001.950164
Filename :
950164
Link To Document :
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