Abstract

This paper proposes and experimentally demonstrates a blind modulation format identification (MFI) method delivering high accuracy (> 99%) even in a low OSNR regime (< 10 dB). By using nonlinear power transformation and peak detection, the proposed MFI can recognize whether the signal modulation format is BPSK, QPSK, 8-PSK or 16-QAM. Experimental results demonstrate that the proposed MFI can achieve a successful identification rate as high as 99% when the incoming signal OSNR is 7 dB. Key parameters, such as FFT length and laser phase noise tolerance of the proposed method, have been characterized.

© 2017 Optical Society of America

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References

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2017 (4)

2016 (1)

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

2015 (3)

2014 (1)

2010 (2)

A. Nag, M. Tornatore, and B. Mukherjee, “Optical network design with mixed line rates and multiple modulation formats,” J. Lightwave Technol. 28(4), 466–475 (2010).

I. Fatadin, D. Ives, and S. Savory, “Laser linewidth tolerance for 16-QAM coherent optical system using QPSK partitioning,” IEEE Photonics Technol. Lett. 22(9), 631–633 (2010).

Al-Arashi, W.

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

Al-Arashi, W. H.

Bilal, S. M.

Bo, T.

T. Bo, J. Tang, and C. Chan, “Modulation format recognition for optical signals using connected component analysis,” IEEE Photonics Technol. Lett. 29(1), 11–14 (2017).

Boada, R.

Borkowski, R.

Bosco, G.

Chan, C.

T. Bo, J. Tang, and C. Chan, “Modulation format recognition for optical signals using connected component analysis,” IEEE Photonics Technol. Lett. 29(1), 11–14 (2017).

DeSalvo, R.

Dong, Z.

Fatadin, I.

I. Fatadin, D. Ives, and S. Savory, “Laser linewidth tolerance for 16-QAM coherent optical system using QPSK partitioning,” IEEE Photonics Technol. Lett. 22(9), 631–633 (2010).

Fu, S.

Guo, C.

He, S.

Huang, L.

Isautier, P.

Ives, D.

I. Fatadin, D. Ives, and S. Savory, “Laser linewidth tolerance for 16-QAM coherent optical system using QPSK partitioning,” IEEE Photonics Technol. Lett. 22(9), 631–633 (2010).

Khan, F.

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

Khan, F. N.

Lau, A.

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

Lau, A. P.

Lau, A. P. T.

Li, Z.

Liu, D.

Liu, J.

Lu, C.

Mai, X.

Monroy, I. T.

Mukherjee, B.

Nag, A.

Pan, J.

Plant, D. V.

Qiu, M.

Ralph, S.

Savory, S.

I. Fatadin, D. Ives, and S. Savory, “Laser linewidth tolerance for 16-QAM coherent optical system using QPSK partitioning,” IEEE Photonics Technol. Lett. 22(9), 631–633 (2010).

Tang, J.

T. Bo, J. Tang, and C. Chan, “Modulation format recognition for optical signals using connected component analysis,” IEEE Photonics Technol. Lett. 29(1), 11–14 (2017).

Tang, M.

Tornatore, M.

Wang, D.

Wu, X.

Xiang, M.

Yang, Y.

Yu, C.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express 25(15), 17767–17776 (2017).
[PubMed]

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

Zhang, Q.

Zhong, K.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express 25(15), 17767–17776 (2017).
[PubMed]

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

Zhou, X.

Zhuge, Q.

IEEE Photonics Technol. Lett. (3)

F. Khan, K. Zhong, W. Al-Arashi, C. Yu, C. Lu, and A. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).

T. Bo, J. Tang, and C. Chan, “Modulation format recognition for optical signals using connected component analysis,” IEEE Photonics Technol. Lett. 29(1), 11–14 (2017).

I. Fatadin, D. Ives, and S. Savory, “Laser linewidth tolerance for 16-QAM coherent optical system using QPSK partitioning,” IEEE Photonics Technol. Lett. 22(9), 631–633 (2010).

J. Lightwave Technol. (2)

Opt. Express (6)

Other (1)

K. Roberts and C. Laperle, “Flexible transceivers,” in European Conference and Exhibition on Optical Communication (2012), paper We.3.A.3.

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Figures (11)

Fig. 1
Fig. 1 Flexible receiver DSP architecture with blind MFI stage. LO: local oscillator; ADC: analog to digital converter. Inset in the top left shows incoming signals with different modulation formats.
Fig. 2
Fig. 2 (a) Constellation diagrams. (b) FFT after ()2. (c) FFT after ()4. (d) FFT after ()8.
Fig. 3
Fig. 3 Distinguish between QPSK and 16-QAM.
Fig. 4
Fig. 4 Probabilities of missed and false alarm versus the PAPR threshold.
Fig. 5
Fig. 5 (a) Experimental setup. VOA: variable optical attenuator. BPF: bandpass filter. OSA: optical spectrum analyzer. PC: polarization controller. EDFA: erbium-doped fiber amplifier. (b) DSP flow of the receiver.
Fig. 6
Fig. 6 (a) Proposed MFI block diagram. (b) Flow chart of the decision tree.
Fig. 7
Fig. 7 Measured probability of correct recognition for 10 Gbaud BPSK, QPSK, 8-PSK, and 16-QAM signals with variant length of FFT.
Fig. 8
Fig. 8 Energy leakage induced from the larger phase noise for 16-QAM signal after 4th power operation.
Fig. 9
Fig. 9 Measured probability of correct recognition for different modulation formats with 100 kHz and 2 MHz linewidth lasers.
Fig. 10
Fig. 10 (a) Measured probability of correct recognition for different launch power. (b) Measured Q-factor for different launch power.
Fig. 11
Fig. 11 Minimum OSNR required for identification of modulation formats for different MFIs CCA: Connected component analysis [8], 32 GBd. NIC: Non-iterative clustering [7], 28 GBd 80-km SMF. HOS: Higher-order statistics [9], 31.5 GBd, 810-km LAF. ML: Maximum likelihood [6], 28 GBd B2B. SPD: Signal power distribution [4], 14 GBd B2B.

Tables (1)

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Table 1 Peak generated by different nonlinear transformations

Equations (9)

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X k = A k e j( ω c k T s + φ k ) + n k
F{X}=F{S}+F{N}
R A [k,k+m]=E[ A k A k+m * ]=E[ A k ]E[ A k+m * ],m[0,1,...N1]
R A [k,k+m]={ 0,m0 1,m=0
( X k ) 2 = A k 2 e 2j( ω c k T s + φ k ) + n k 2 +2 A k n k e j( ω c k T s + φ k )
F{X}=F{S}+F{N}+F{ N ' }
R A [k,k+m]=1,m[0,1,...N1]
S A (f)=δ(f)
PAPR= max(|F{ X 2or4or8 }|) mean(|F{ X 2or4or8 }|)

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