Name
Abstract | ||
|---|---|---|
This paper introduces an algorithm that extends the capability of digital modulations classifiers to cope with signals that have memory incorporated in their modulation scheme. The algorithm employs the decision-theoretic approach where the identification of different modulation types is performed by developing a set of decision criteria. The performance of the classifier has been evaluated by simulating different types of bandlimited digital signals corrupted by Gaussian noise. It is shown that the overall success rate is over 94% at the signal to noise ratio (SNR) of 10 dB with some modulation schemes detected with success rate of 100% at this SNR |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/ISSPA.1999.815781 | Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium |
Keywords | Field | DocType |
Gaussian noise,bandlimited signals,decision theory,digital signals,feature extraction,modulation,signal classification,Gaussian noise,MSK,SNR,algorithm,automatic recognition,bandlimited digital signals,classifier performance,decision criteria,decision theory,digital modulation classifiers,digitally modulated communications signals,key feature extraction,memory,modulation type identification,signal to noise ratio,simulation,success rate | Quadrature amplitude modulation,Pattern recognition,Computer science,Digital signal,Signal-to-noise ratio,Delta modulation,Speech recognition,Delta-sigma modulation,Analog transmission,Artificial intelligence,Pulse-amplitude modulation,Gaussian noise | Conference |
Volume | ISBN | Citations |
2 | 1-86435-451-8 | 1 |
PageRank | References | Authors |
0.68 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramakomar, V. | 1 | 1 | 0.68 |
| Daryoush Habibi | 2 | 82 | 20.85 |
| Abdesselam Bouzerdoum | 3 | 883 | 89.51 |