Name
Abstract | ||
|---|---|---|
Automatic modulation classification (or recognition) is an intrinsically interesting problem with a variety of regulatory and military applications. We developed a method which is simple, fast, efficient and robust. The feature being used is the counts of signals falling into different parts of the signal plane. Compared with the likelihood method and the High Order Correlation method, it is much easier to be implemented, and the execution is much faster. When the channel model is correct, our method is efficient, in the sense that it will achieve the "optimal" classification rate. When unknown contamination is present, our method can automatically overcome to certain degree. At SNR being 10 and 15dB, examples of classifying two modulation types-QAM4 and PSK6-are given. Simulations demonstrate its ability to deal with unknown noises. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICASSP.1998.679567 | PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6 |
Keywords | Field | DocType |
quadrature amplitude modulation,magnetohydrodynamics,noise,signal to noise ratio,correlation,phase shift keying,contamination,counting,gaussian noise,geometry,statistics,snr | Channel models,Quadrature amplitude modulation,Pattern recognition,Computer science,Signal-to-noise ratio,Modulation,Correlation,Artificial intelligence,Gaussian noise,Classification rate,Phase-shift keying | Conference |
ISSN | Citations | PageRank |
1520-6149 | 9 | 1.11 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Huo | 1 | 157 | 24.83 |
| D. L. Donoho | 2 | 9350 | 1189.81 |