Abstract

Automatic modulation recognition (AMR) is an integral part of an intelligent transceiver for future underwater optical wireless communications (UOWC). In this paper, an orthogonal frequency division multiplexing (OFDM) based progressive growth meta-learning (PGML) AMR scheme is proposed and analyzed over UOWC turbulence channels. The novel PGML few-shot AMR framework, mainly suffering from the severe underwater environments, can achieve fast self-learning for new tasks with less training time and data. In the PGML algorithm, the few-shot classifier, which works in the presence of Poisson noise, is fed with constellations of noisy signals in bad signal-to-noise ratio (SNR) scenarios directly. Moreover, the data augmentation (DA) operation is adopted to mitigate the impact of light-emitting diode (LED) distortion, yielding further classification accuracy improvements. Simulation results demonstrate that the proposed PGML scheme outperforms the classical meta-learning (ML) approach in training efficiency, robustness against Poisson noise and generalization performance on a new task.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
More Like This
Transfer learning assisted convolutional neural networks for modulation format recognition in few-mode fibers

Xiaorong Zhu, Bo Liu, Xu Zhu, Jianxin Ren, Rahat Ullah, Yaya Mao, Xiangyu Wu, Mingye Li, Shuaidong Chen, and Yu Bai
Opt. Express 29(22) 36953-36963 (2021)

Impact of angular pointing error on BER performance of underwater optical wireless links

Rubén Boluda-Ruiz, Antonio García-Zambrana, Beatriz Castillo-Vázquez, and Steve Hranilovic
Opt. Express 28(23) 34606-34622 (2020)

34.5 m underwater optical wireless communication with 2.70 Gbps data rate based on a green laser diode with NRZ-OOK modulation

Xiaoyan Liu, Suyu Yi, Xiaolin Zhou, Zhilai Fang, Zhi-Jun Qiu, Laigui Hu, Chunxiao Cong, Lirong Zheng, Ran Liu, and Pengfei Tian
Opt. Express 25(22) 27937-27947 (2017)

View More...

References

  • View by:
  • Article Order
  • Year
  • Author
  • Publication
  1. T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
    [Crossref]
  2. J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
    [Crossref]
  3. Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
    [Crossref]
  4. J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
    [Crossref]
  5. D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
    [Crossref]
  6. C. Finn, “Learning to learn with gradients,” Ph.D. thesis, UC Berkeley (2018).
  7. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 (2017).
  8. X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
    [Crossref]
  9. S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
    [Crossref]
  10. T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
    [Crossref]
  11. S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
    [Crossref]
  12. A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
    [Crossref]
  13. X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
    [Crossref]
  14. H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
    [Crossref]
  15. J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
    [Crossref]
  16. F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
    [Crossref]
  17. K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
    [Crossref]
  18. K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
    [Crossref]
  19. Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
    [Crossref]
  20. M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
    [Crossref]
  21. M. F. Sanya, L. Djogbe, A. Vianou, and C. Aupetit-Berthelemot, “Dc-biased optical ofdm for im/dd passive optical network systems,” J. Opt. Commun. Netw. 7(4), 205–214 (2015).
    [Crossref]
  22. S. D. Dissanayake and J. Armstrong, “Comparison of aco-ofdm, dco-ofdm and ado-ofdm in im/dd systems,” J. Lightwave Technol. 31(7), 1063–1072 (2013).
    [Crossref]
  23. W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
    [Crossref]
  24. Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
    [Crossref]
  25. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics, (2011), pp. 315–323.
  26. S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
    [Crossref]
  27. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

2020 (11)

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
[Crossref]

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

2019 (4)

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
[Crossref]

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

2018 (2)

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

2017 (2)

J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref]

2015 (3)

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

M. F. Sanya, L. Djogbe, A. Vianou, and C. Aupetit-Berthelemot, “Dc-biased optical ofdm for im/dd passive optical network systems,” J. Opt. Commun. Netw. 7(4), 205–214 (2015).
[Crossref]

W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
[Crossref]

2013 (1)

Abbeel, P.

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 (2017).

Alwageed, H.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Armstrong, J.

Arulandu, K.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Aupetit-Berthelemot, C.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Bengio, Y.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics, (2011), pp. 315–323.

Bordes, A.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics, (2011), pp. 315–323.

Bu, K.

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

Cao, C.

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Chen, B.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Chen, C.

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

Chen, C. L. P.

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

Chen, P.

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

Chen, X.

Cui, J.

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

Deng, X.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Dissanayake, S. D.

Djogbe, L.

Du, X.

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

Finn, C.

C. Finn, “Learning to learn with gradients,” Ph.D. thesis, UC Berkeley (2018).

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 (2017).

Ginanjar, R. R.

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

Glorot, X.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics, (2011), pp. 315–323.

Gui, G.

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

Gulliver, T. A.

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Han, J.

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

He, J.

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

He, Y.

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

Hermawan, A. P.

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

Hu, H.

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

Hu, S.

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

Iwahori, Y.

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

Ji, Y.-W.

Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
[Crossref]

Jiang, H.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Jiang, K.

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

Jiang, M.

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

Jin, J.

H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
[Crossref]

Jing, X.

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

Khalid, A. M.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Kim, D.

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Lain, J.

J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
[Crossref]

Lee, J.

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

Levine, S.

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 (2017).

Li, B.

J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
[Crossref]

Li, J.

Li, X.

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

Li, Z.

Liang, P. P.

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

Liang, X.

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

Liang, Y.

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

Liang, Y.-C.

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

Linnartz, J. M. G.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Liu, J.

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

Liu, M.

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

Liu, W.

W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
[Crossref]

Lu, H.

H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
[Crossref]

Mardanikorani, S.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Meng, F.

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

Pan, M.

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

Pei, Y.

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

Peng, S.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Qiu, T.

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

Sang, Z.

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Sanya, M. F.

Sebdani, M. M.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Shang, X.

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

Sheu, J.

J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
[Crossref]

Shi, J.

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

Shuai, C.

T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
[Crossref]

Song, C.

Tang, Q.

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

Vianou, A.

Wang, A.

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

Wang, C.

Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
[Crossref]

Wang, D.

Wang, H.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Wang, J.

H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
[Crossref]

Wang, X.

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

Wang, Y.

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Wei, D.

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

Wu, G.-F.

Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
[Crossref]

Wu, H.

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

Wu, L.

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

Wu, Y.

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

Xu, L.

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Xu, T.

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

Xu, Z.

W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
[Crossref]

Yang, J.

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

Yang, L.

W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
[Crossref]

Yao, Y.

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Zhang, H.

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

Zhang, J.

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

Zhang, M.

Zhang, T.

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
[Crossref]

Zhao, C.

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

Zhao, Z.

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

Zhou, T.

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

Zhou, Y.

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
[Crossref]

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

Appl. Sci. (1)

K. Jiang, J. Zhang, H. Wu, A. Wang, and Y. Iwahori, “A novel digital modulation recognition algorithm based on deep convolutional neural network,” Appl. Sci. 10(3), 1166 (2020).
[Crossref]

Comput. Intell. Neurosci. (1)

Y. Wang, H. Zhang, Z. Sang, L. Xu, C. Cao, and T. A. Gulliver, “Modulation classification of underwater communication with deep learning network,” Comput. Intell. Neurosci. 2019, 1–15 (2019).
[Crossref]

IEEE Access (2)

X. Shang, H. Hu, X. Li, T. Xu, and T. Zhou, “Dive into deep learning based automatic modulation classification: A disentangled approach,” IEEE Access 8, 113271–113284 (2020).
[Crossref]

T. Zhang, C. Shuai, and Y. Zhou, “Deep learning for robust automatic modulation recognition method for iot applications,” IEEE Access 8, 117689–117697 (2020).
[Crossref]

IEEE Commun. Lett. (1)

A. P. Hermawan, R. R. Ginanjar, D. Kim, and J. Lee, “Cnn-based automatic modulation classification for beyond 5g communications,” IEEE Commun. Lett. 24(5), 1038–1041 (2020).
[Crossref]

IEEE Internet Things J. (1)

J. Liu, X. Du, J. Cui, M. Pan, and D. Wei, “Task-oriented intelligent networking architecture for the spac-air-ground-aqua integrated network,” IEEE Internet Things J. 7(6), 5345–5358 (2020).
[Crossref]

IEEE Photonics J. (1)

Y.-W. Ji, G.-F. Wu, and C. Wang, “Generalized likelihood block detection for spad-based underwater vlc system,” IEEE Photonics J. 12(1), 1–10 (2020).
[Crossref]

IEEE Photonics Technol. Lett. (2)

M. Jiang, J. Zhang, X. Liang, and C. Zhao, “Direct current bias optimization of the ldpc coded dco-ofdm systems,” IEEE Photonics Technol. Lett. 27(19), 2095–2098 (2015).
[Crossref]

J. He, Y. Zhou, J. Shi, and Q. Tang, “Modulation classification method based on clustering and gaussian model analysis for vlc system,” IEEE Photonics Technol. Lett. 32(11), 651–654 (2020).
[Crossref]

IEEE Signal Process. Lett. (1)

K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Process. Lett. 27, 880–884 (2020).
[Crossref]

IEEE Trans. Commun. (1)

X. Deng, S. Mardanikorani, Y. Wu, K. Arulandu, B. Chen, A. M. Khalid, and J. M. G. Linnartz, “Mitigating led nonlinearity to enhance visible light communications,” IEEE Trans. Commun. 66(11), 5593–5607 (2018).
[Crossref]

IEEE Trans. Ind. Inf. (1)

T. Qiu, Z. Zhao, T. Zhang, C. Chen, and C. L. P. Chen, “Underwater internet of things in smart ocean: System architecture and open issues,” IEEE Trans. Ind. Inf. 16(7), 4297–4307 (2020).
[Crossref]

IEEE Trans. Neural Netw. Learning Syst. (1)

S. Peng, H. Jiang, H. Wang, H. Alwageed, Y. Zhou, M. M. Sebdani, and Y. Yao, “Modulation classification based on signal constellation diagrams and deep learning,” IEEE Trans. Neural Netw. Learning Syst. 30(3), 718–727 (2019).
[Crossref]

IEEE Trans. Veh. Technol. (4)

Y. Wang, M. Liu, J. Yang, and G. Gui, “Data-driven deep learning for automatic modulation recognition in cognitive radios,” IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

S. Hu, Y. Pei, P. P. Liang, and Y.-C. Liang, “Deep neural network for robust modulation classification under uncertain noise conditions,” IEEE Trans. Veh. Technol. 69(1), 564–577 (2020).
[Crossref]

F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. 67(11), 10760–10772 (2018).
[Crossref]

IET Commun. (1)

H. Lu, J. Jin, and J. Wang, “Alleviation of LED nonlinearity impact in visible light communication using companding and predistortion,” IET Commun. 13(7), 818–821 (2019).
[Crossref]

IET Optoelectron. (1)

J. Sheu, B. Li, and J. Lain, “Led non-linearity mitigation techniques for optical ofdm-based visible light communications,” IET Optoelectron. 11(6), 259–264 (2017).
[Crossref]

J. Lightwave Technol. (1)

J. Opt. Commun. Netw. (1)

Opt. Express (1)

Photonics Res. (1)

W. Liu, Z. Xu, and L. Yang, “Simo detection schemes for underwater optical wireless communication under turbulence,” Photonics Res. 3(3), 48–53 (2015).
[Crossref]

Other (4)

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics, (2011), pp. 315–323.

C. Finn, “Learning to learn with gradients,” Ph.D. thesis, UC Berkeley (2018).

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 (2017).

Cited By

Optica participates in Crossref's Cited-By Linking service. Citing articles from Optica Publishing Group journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (11)
Fig. 1.
Fig. 1. The block diagram of our simulated smart UOWC AMR system. OFDM Tx: a typical DC-biased optical OFDM transmitter; OFDM Rx: an OFDM receiver with a PGML few-shot classifier.

Download Full Size | PPT Slide | PDF

Fig. 2.
Fig. 2. (a) Image of the micro-LED array; (b) P-I-V curve of the GaN-based micro-LED array.

Download Full Size | PPT Slide | PDF

Fig. 3.
Fig. 3. Illustration of the proposed few-shot classifier based smart AMR system. In one task, types of constellations are mutually exclusive in training/validation datasets (e.g., $\boldsymbol {\chi _{1}}, \boldsymbol {\chi _{2}}$ and $\boldsymbol {\chi _3}, \boldsymbol {\chi _4}$). Constellations diagrams in support/query sets are denoted by $[\chi _i^{j}(1),\chi _i^{j}(2),\ldots ,\chi _i^{j}(28\times 28)]$, where $j\in [1,20]$ is an integer corresponding to different input sample. Besides, $\beta$ is a hyperparameter, being set to $1.0\times 10^{-3}$.

Download Full Size | PPT Slide | PDF

Fig. 4.
Fig. 4. The division of the training/validation dataset ($D^{tr}$/$D^{val}$). The entire dataset contains 15 modulation types, among which eight classes for training and seven classes for validation. In one task, constellations in support/query set are of the same class but different samples.

Download Full Size | PPT Slide | PDF

Fig. 5.
Fig. 5. DA operation and datasets preprocessing.

Download Full Size | PPT Slide | PDF

Fig. 6.
Fig. 6. Accuracy vs. epoch times. Training accuracy on prior tasks drawn from dataset $D^{tr}$, including $M$-ary ASK ($M$= $2^{n}$, $n$= 1), $M$-ary PSK ($M$= $2^{n}$, $n$= $2, 3, 4, 5)$, and $M$-ary QAM ($M$= $2^{n}$, $n$= $6, 7, 8)$ modulation constellations. Validation accuracy on new tasks drawn from dataset $D^{val}$, including $M$-ary QAM ($M$= $2^{n}$, $n$= $2, 3, 4, 5)$, $M$-ary ASK ($M$= $2^{n}$, $n$= $2, 3)$, and BPSK modulation constellations. Classification models are trained in different SNR scenarios: (a) SNR= 6 dB; (b) SNR= 9 dB; (c) SNR= 12 dB; (d) SNR= $j$ dB ($j\in$[6 dB, 12 dB]).

Download Full Size | PPT Slide | PDF

Fig. 7.
Fig. 7. Classification performance comparison curves between baseline classical ML method and the proposed PGML scheme under SNR range being [6, 15] dB, based on saved models: (a) Model $M_{bad}$; (b) Model $M_{median}$; (c) Model $M_{high}$; (d) Model $M_{mix}$.

Download Full Size | PPT Slide | PDF

Fig. 8.
Fig. 8. $P_{cc}$ curves of model $M_{bad}$ and $M_{high}$ versus SNR in range [6, 15] dB. Classification performance with DA operation adopted are plotted with dotted lines, and it illustrated with solid lines when implemented on original datasets: (a) Model $M_{bad}$; (b) Model $M_{high}$.

Download Full Size | PPT Slide | PDF

Fig. 9.
Fig. 9. Training time consumption comparison between the proposed PGML scheme and classical ML method. The total time recorded under four SNR scenarios (SNR= 6 dB, SNR= 9 dB, SNR= 12 dB and mixed SNRs) is averaged as $T_{av}$.

Download Full Size | PPT Slide | PDF

Fig. 10.
Fig. 10. BERs for $M$-ary QAM ($M$= $2^{n}$, $n$ = 2, 3,…, 7, 8) and BPSK modulation formats versus transmit energy in (dBJ).

Download Full Size | PPT Slide | PDF

Fig. 11.
Fig. 11. (a) The received constellation diagrams for five modulation categories (BPSK, $M$-ary QAM ($M= 2^{n}$, $n$= 2, 3, 4)) with different SNRs over UOWC experiments. The $P_{cc}$ curves of the proposed PGML scheme and the classical method under varying received SNRs validated on the trained models: (b) $M_{bad}$; (c) $M_{high}$.

Download Full Size | PPT Slide | PDF

Tables (2)

Tables Icon

Table 1. Settings in the proposed framework

View Table | View all tables in this article
Tables Icon

Table 2. Parameters setting in the simulation

View Table | View all tables in this article

Equations (13)

Equations on this page are rendered with MathJax. Learn more.

x k = ∑ m = 0 2 N − 1 X m exp ( j 2 π k m 2 N ) , k = 0 , 1 , 2 , … , 2 N − 1 ,
I r = I t ⋅ h a ⋅ h f ,
h a = exp [ − c ( λ ) L ] ,
f ( h f ) = 1 h f σ f 2 π exp [ − ( l n ( h f / I 0 ) − μ f ) 2 2 σ f 2 ] ,
σ f 2 = exp [ 0.49 σ r 2 ( 1 + 1.11 σ r 12 / 5 ) 7 / 6 + 0.51 σ r 2 ( 1 + 0.69 σ r 12 / 5 ) 5 / 6 ] − 1 ,
n r [ k ] = n s h a h f x ^ b [ k ] + n b ,
P r ( r [ k ] ) = ( n s h a h f x ^ b [ k ] + n b ) r [ k ] r [ k ] ! exp ( n s h a h f x ^ b [ k ] + n b ) .
f W , B ( χ i , j ) = χ i , j l = W χ i , j l − 1 + B ,
p ^ i = P ( y o u t , i = y t a r , 1 , y t a r , 2 | χ i , j l ) = softmax ( χ i , j l ) = [ e y i , 1 ∑ k = 1 2 e y i , k , e y i , 2 ∑ k = 1 2 e y i , k ] ,
L ( f W , B ) = − ∑ i = 1 2 q i log [ softmax ( f W , B ( χ i , j ) ) ] ,
Θ ′ = Θ − γ ▽ Θ L T p , m b ( D s t r , f Θ ) ,
Θ ∗ = Θ − β ▽ Θ 1 M b ∑ m b = 1 M b L T p , m b ( D q t r , f Θ ′ ) ,
Θ ∗ = Θ − β M b ∑ m b = 1 M b ▽ Θ L T p , m b ( D q t r , f [ Θ − γ ▽ Θ L T p , m b ( D s t r , f Θ ) ] ) ⋅ [ I − γ ▽ Θ 2 L T p , m b ( D s t r , f Θ ) ] ,

Metrics