Name
Title | ||
|---|---|---|
New Automatic Modulation Classifier Using Cyclic-Spectrum Graphs With Optimal Training Features. |
Abstract | ||
|---|---|---|
A new feature-extraction paradigm for graph-based automatic modulation classification is proposed in this letter. In the proposed new framework, the modulation features are optimally constructed using the Kullback-Leibler divergence of the dominant entries in the adjacency matrices associated with the graph presentation of the cyclic spectra. Then, the Hamming distance is invoked to measure the di... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LCOMM.2018.2819991 | IEEE Communications Letters |
Keywords | Field | DocType |
Modulation,Training,Feature extraction,Hamming distance,Monte Carlo methods,Indexes,Manuals | Adjacency matrix,Graph,Monte Carlo method,Computer science,Algorithm,Feature extraction,Real-time computing,Modulation,Hamming distance,Test data,Classifier (linguistics) | Journal |
Volume | Issue | ISSN |
22 | 6 | 1089-7798 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Yan | 1 | 7 | 1.48 |
| Guannan Liu | 2 | 131 | 10.78 |
| Hsiao-chun Wu | 3 | 959 | 97.99 |
| Guoyu Feng | 4 | 1 | 0.70 |