Stokes Space-Based Modulation Format Recognition for Autonomous Optical Receivers

Isautier, Pierre; Pan, Jie; DeSalvo, Richard; Ralph, Stephen E.

doi:10.1109/jlt.2015.2492858

Cited by 37 publications

(27 citation statements)

References 22 publications

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“…Nonlinear phase noise compensation: [18][19][20][21][22] Modulation format identification: [10][11] Optical signal monitoring: [13][14][15] Nonlinearity compensation: [23] Amplitude, phase and nonlinear phase noise: [3][4][5][6][7][8] Optical performance monitoring: [12,[16][17], Nonlinear phase noise compensation: [19,22] Cognitive receiver design:…”

Section: Clustering K-meansmentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Thrane

Wass

Piels

et al. 2017

J. Lightwave Technol.

147

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Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio (OSNR) estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation (QAM), at 32 Gbaud, using only directly-detected data.

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Section: Clustering K-meansmentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Thrane

Wass

Piels

et al. 2017

J. Lightwave Technol.

147

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show abstract

“…One important building block of a flexible receiver is the modulation format identification (MFI) module, which tracks the modulation format of the received signal and reconfigures the digital signal processing (DSP) circuit of the receiver accordingly. As discussed in [3][4][5][6][7][8][9], this automatic recognition functionality removes the need for end-to-end handshaking between the transmitter (Tx) and receiver (Rx).…”

Section: Introductionmentioning

confidence: 99%

“…Various MFI techniques have been proposed recently [3][4][5][6][7][8][9]. For example, Bilal et al introduced a blind MFI by evaluation the peak-to-average power ratio (PAPR) of the incoming samples after chromatic dispersion (CD) compensation and constant modulus algorithm (CMA) equalization [4].…”

Section: Introductionmentioning

confidence: 99%

Blind modulation format identification using nonlinear power transformation

et al. 2017

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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.

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“…In this scenario, modulation format identification (MFI) is essential for reconfiguring digital signal processing (DSP) for signal recovery and demapping at the receiver-side (Rx). Recently, several MFI techniques based on the properties of specific standard QAM formats have been proposed [7][8][9][10][11][12][13]. For example, MFI can be achieved in the Stokes space by identifying either the number of clusters or the higher order statistics [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several MFI techniques based on the properties of specific standard QAM formats have been proposed [7][8][9][10][11][12][13]. For example, MFI can be achieved in the Stokes space by identifying either the number of clusters or the higher order statistics [7][8][9][10]. It can also be implemented based on the power distributions of the received signals [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Modulation format identification aided hitless flexible coherent transceiver

Xiang¹,

Zhuge²,

Qiu³

et al. 2016

Opt. Express

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We propose a hitless flexible coherent transceiver enabled by a novel modulation format identification (MFI) scheme for dynamic agile optical networks. The modulation format transparent digital signal processing (DSP) is realized by a block-wise decision-directed least-mean-square (DD-LMS) equalizer for channel tracking, and a pilot symbol aided superscalar phase locked loop (PLL) for carrier phase estimation (CPE). For the MFI, the modulation format information is encoded onto the pilot symbols initially used for CPE. Therefore, the proposed MFI method does not require extra overhead. Moreover, it can identify arbitrary modulation formats including multi-dimensional formats, and it enables tracking of the format change for short data blocks. The performance of the proposed hitless flexible coherent transceiver is successfully evaluated with five modulation formats including QPSK, 16QAM, 64QAM, Hybrid QPSK/8QAM and set-partitioning (SP)-512-QAM. We show that the proposed MFI method induces a negligible performance penalty. Moreover, we experimentally demonstrate that such a hitless transceiver can adapt to fast block-by-block modulation format switching. Finally, the performance improvement of the proposed MFI method is experimentally verified with respect to other commonly used MFI methods.

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Stokes Space-Based Modulation Format Recognition for Autonomous Optical Receivers

Cited by 37 publications

References 22 publications

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Blind modulation format identification using nonlinear power transformation

Modulation format identification aided hitless flexible coherent transceiver

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