Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants
Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel est...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Sai [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Automatic modulation classification (AMC) |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on vehicular technology - New York, NY : IEEE, 1967, 66(2017), 7, Seite 6089-6101 |
|---|---|
| Übergeordnetes Werk: |
volume:66 ; year:2017 ; number:7 ; pages:6089-6101 |
| Links: |
|---|
| DOI / URN: |
10.1109/TVT.2016.2636324 |
|---|
| Katalog-ID: |
OLC1995847380 |
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| 520 | |a Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. | ||
| 650 | 4 | |a Blind equalizers | |
| 650 | 4 | |a multiple cumulants | |
| 650 | 4 | |a Channel estimation | |
| 650 | 4 | |a Modulation | |
| 650 | 4 | |a Automatic modulation classification (AMC) | |
| 650 | 4 | |a Wireless communication | |
| 650 | 4 | |a Maximum likelihood estimation | |
| 650 | 4 | |a overlapped signal classification | |
| 650 | 4 | |a Monitoring | |
| 650 | 4 | |a spectrum monitoring | |
| 650 | 4 | |a Receiving antennas | |
| 650 | 4 | |a maximum likelihood (ML) classification | |
| 700 | 1 | |a Yao, Yuanyuan |4 oth | |
| 700 | 1 | |a Wei, Zhiqing |4 oth | |
| 700 | 1 | |a Feng, Zhiyong |4 oth | |
| 700 | 1 | |a Zhang, Ping |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE transactions on vehicular technology |d New York, NY : IEEE, 1967 |g 66(2017), 7, Seite 6089-6101 |w (DE-627)129358584 |w (DE-600)160444-2 |w (DE-576)014730871 |x 0018-9545 |7 nnns |
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10.1109/TVT.2016.2636324 doi PQ20170901 (DE-627)OLC1995847380 (DE-599)GBVOLC1995847380 (PRQ)c1066-9662ae5b3a793fc9d723a7ea777e0ec538fdce80ced00d6364c7f57777a508580 (KEY)0030991520170000066000706089automaticmodulationclassificationofoverlappedsourc DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Huang, Sai verfasserin aut Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification Yao, Yuanyuan oth Wei, Zhiqing oth Feng, Zhiyong oth Zhang, Ping oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 66(2017), 7, Seite 6089-6101 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:66 year:2017 number:7 pages:6089-6101 http://dx.doi.org/10.1109/TVT.2016.2636324 Volltext http://ieeexplore.ieee.org/document/7776899 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 66 2017 7 6089-6101 |
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10.1109/TVT.2016.2636324 doi PQ20170901 (DE-627)OLC1995847380 (DE-599)GBVOLC1995847380 (PRQ)c1066-9662ae5b3a793fc9d723a7ea777e0ec538fdce80ced00d6364c7f57777a508580 (KEY)0030991520170000066000706089automaticmodulationclassificationofoverlappedsourc DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Huang, Sai verfasserin aut Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification Yao, Yuanyuan oth Wei, Zhiqing oth Feng, Zhiyong oth Zhang, Ping oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 66(2017), 7, Seite 6089-6101 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:66 year:2017 number:7 pages:6089-6101 http://dx.doi.org/10.1109/TVT.2016.2636324 Volltext http://ieeexplore.ieee.org/document/7776899 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 66 2017 7 6089-6101 |
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10.1109/TVT.2016.2636324 doi PQ20170901 (DE-627)OLC1995847380 (DE-599)GBVOLC1995847380 (PRQ)c1066-9662ae5b3a793fc9d723a7ea777e0ec538fdce80ced00d6364c7f57777a508580 (KEY)0030991520170000066000706089automaticmodulationclassificationofoverlappedsourc DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Huang, Sai verfasserin aut Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification Yao, Yuanyuan oth Wei, Zhiqing oth Feng, Zhiyong oth Zhang, Ping oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 66(2017), 7, Seite 6089-6101 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:66 year:2017 number:7 pages:6089-6101 http://dx.doi.org/10.1109/TVT.2016.2636324 Volltext http://ieeexplore.ieee.org/document/7776899 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 66 2017 7 6089-6101 |
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10.1109/TVT.2016.2636324 doi PQ20170901 (DE-627)OLC1995847380 (DE-599)GBVOLC1995847380 (PRQ)c1066-9662ae5b3a793fc9d723a7ea777e0ec538fdce80ced00d6364c7f57777a508580 (KEY)0030991520170000066000706089automaticmodulationclassificationofoverlappedsourc DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Huang, Sai verfasserin aut Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification Yao, Yuanyuan oth Wei, Zhiqing oth Feng, Zhiyong oth Zhang, Ping oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 66(2017), 7, Seite 6089-6101 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:66 year:2017 number:7 pages:6089-6101 http://dx.doi.org/10.1109/TVT.2016.2636324 Volltext http://ieeexplore.ieee.org/document/7776899 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 66 2017 7 6089-6101 |
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10.1109/TVT.2016.2636324 doi PQ20170901 (DE-627)OLC1995847380 (DE-599)GBVOLC1995847380 (PRQ)c1066-9662ae5b3a793fc9d723a7ea777e0ec538fdce80ced00d6364c7f57777a508580 (KEY)0030991520170000066000706089automaticmodulationclassificationofoverlappedsourc DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Huang, Sai verfasserin aut Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification Yao, Yuanyuan oth Wei, Zhiqing oth Feng, Zhiyong oth Zhang, Ping oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 66(2017), 7, Seite 6089-6101 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:66 year:2017 number:7 pages:6089-6101 http://dx.doi.org/10.1109/TVT.2016.2636324 Volltext http://ieeexplore.ieee.org/document/7776899 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 66 2017 7 6089-6101 |
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620 DNB 53.70 bkl 53.74 bkl Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants Blind equalizers multiple cumulants Channel estimation Modulation Automatic modulation classification (AMC) Wireless communication Maximum likelihood estimation overlapped signal classification Monitoring spectrum monitoring Receiving antennas maximum likelihood (ML) classification |
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ddc 620 bkl 53.70 bkl 53.74 misc Blind equalizers misc multiple cumulants misc Channel estimation misc Modulation misc Automatic modulation classification (AMC) misc Wireless communication misc Maximum likelihood estimation misc overlapped signal classification misc Monitoring misc spectrum monitoring misc Receiving antennas misc maximum likelihood (ML) classification |
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ddc 620 bkl 53.70 bkl 53.74 misc Blind equalizers misc multiple cumulants misc Channel estimation misc Modulation misc Automatic modulation classification (AMC) misc Wireless communication misc Maximum likelihood estimation misc overlapped signal classification misc Monitoring misc spectrum monitoring misc Receiving antennas misc maximum likelihood (ML) classification |
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ddc 620 bkl 53.70 bkl 53.74 misc Blind equalizers misc multiple cumulants misc Channel estimation misc Modulation misc Automatic modulation classification (AMC) misc Wireless communication misc Maximum likelihood estimation misc overlapped signal classification misc Monitoring misc spectrum monitoring misc Receiving antennas misc maximum likelihood (ML) classification |
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Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants |
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automatic modulation classification of overlapped sources using multiple cumulants |
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Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants |
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Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. |
| abstractGer |
Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. |
| abstract_unstemmed |
Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm. |
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Yao, Yuanyuan Wei, Zhiqing Feng, Zhiyong Zhang, Ping |
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