Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification
Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Qinghe [verfasserIn] Zhao, Penghui [verfasserIn] Li, Yang [verfasserIn] Wang, Hongjun [verfasserIn] Yang, Yang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 33(2020), 13 vom: 19. Nov., Seite 7723-7745 |
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| Übergeordnetes Werk: |
volume:33 ; year:2020 ; number:13 ; day:19 ; month:11 ; pages:7723-7745 |
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| DOI / URN: |
10.1007/s00521-020-05514-1 |
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| Katalog-ID: |
SPR044382014 |
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| 520 | |a Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. | ||
| 650 | 4 | |a Automatic modulation classification (AMC) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data augmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spectrum interference |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhao, Penghui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hongjun |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yang |e verfasserin |4 aut | |
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10.1007/s00521-020-05514-1 doi (DE-627)SPR044382014 (SPR)s00521-020-05514-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zheng, Qinghe verfasserin aut Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 Zhao, Penghui verfasserin aut Li, Yang verfasserin aut Wang, Hongjun verfasserin aut Yang, Yang verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 33(2020), 13 vom: 19. Nov., Seite 7723-7745 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:33 year:2020 number:13 day:19 month:11 pages:7723-7745 https://dx.doi.org/10.1007/s00521-020-05514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 33 2020 13 19 11 7723-7745 |
| spelling |
10.1007/s00521-020-05514-1 doi (DE-627)SPR044382014 (SPR)s00521-020-05514-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zheng, Qinghe verfasserin aut Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 Zhao, Penghui verfasserin aut Li, Yang verfasserin aut Wang, Hongjun verfasserin aut Yang, Yang verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 33(2020), 13 vom: 19. Nov., Seite 7723-7745 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:33 year:2020 number:13 day:19 month:11 pages:7723-7745 https://dx.doi.org/10.1007/s00521-020-05514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 33 2020 13 19 11 7723-7745 |
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10.1007/s00521-020-05514-1 doi (DE-627)SPR044382014 (SPR)s00521-020-05514-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zheng, Qinghe verfasserin aut Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 Zhao, Penghui verfasserin aut Li, Yang verfasserin aut Wang, Hongjun verfasserin aut Yang, Yang verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 33(2020), 13 vom: 19. Nov., Seite 7723-7745 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:33 year:2020 number:13 day:19 month:11 pages:7723-7745 https://dx.doi.org/10.1007/s00521-020-05514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 33 2020 13 19 11 7723-7745 |
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10.1007/s00521-020-05514-1 doi (DE-627)SPR044382014 (SPR)s00521-020-05514-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zheng, Qinghe verfasserin aut Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 Zhao, Penghui verfasserin aut Li, Yang verfasserin aut Wang, Hongjun verfasserin aut Yang, Yang verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 33(2020), 13 vom: 19. Nov., Seite 7723-7745 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:33 year:2020 number:13 day:19 month:11 pages:7723-7745 https://dx.doi.org/10.1007/s00521-020-05514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 33 2020 13 19 11 7723-7745 |
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10.1007/s00521-020-05514-1 doi (DE-627)SPR044382014 (SPR)s00521-020-05514-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Zheng, Qinghe verfasserin aut Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 Zhao, Penghui verfasserin aut Li, Yang verfasserin aut Wang, Hongjun verfasserin aut Yang, Yang verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 33(2020), 13 vom: 19. Nov., Seite 7723-7745 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:33 year:2020 number:13 day:19 month:11 pages:7723-7745 https://dx.doi.org/10.1007/s00521-020-05514-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE AR 33 2020 13 19 11 7723-7745 |
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Automatic modulation classification (AMC) Deep learning Data augmentation Spectrum interference |
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Zheng, Qinghe @@aut@@ Zhao, Penghui @@aut@@ Li, Yang @@aut@@ Wang, Hongjun @@aut@@ Yang, Yang @@aut@@ |
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In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. 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Zheng, Qinghe |
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004 ASE 54.72 bkl Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification Automatic modulation classification (AMC) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Spectrum interference (dpeaa)DE-He213 |
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ddc 004 bkl 54.72 misc Automatic modulation classification (AMC) misc Deep learning misc Data augmentation misc Spectrum interference |
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spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification |
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Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification |
| abstract |
Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstractGer |
Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method. © Springer-Verlag London Ltd., part of Springer Nature 2020 |
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Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification |
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https://dx.doi.org/10.1007/s00521-020-05514-1 |
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Zhao, Penghui Li, Yang Wang, Hongjun Yang, Yang |
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Zhao, Penghui Li, Yang Wang, Hongjun Yang, Yang |
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10.1007/s00521-020-05514-1 |
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| score |
7.4018154 |