Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM

Jarajreh, Mutsam A.; Giacoumidis, Elias; Aldaya, Ivan; Le, Son Thai; Tsokanos, Athanasios; Ghassemlooy, Zabih; Doran, Nick

doi:10.1109/lpt.2014.2375960

Cited by 167 publications

(101 citation statements)

References 13 publications

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“…Moreover, albeit the Kerr-induced nonlinear process is deterministic, in multicarrier schemes like coherent optical OFDM (CO-OFDM) the resulting nonlinear interaction between subcarriers becomes very complicated appearing random due to its high peak-toaverage power ratio (PAPR) [3]. Recently, unsupervised and supervised machine learning such as K-means clustering [4] and artificial neural network classification [3] have been introduced in optical communications to combat stochastic source of noises, performing blind and non-blind NLE, respectively. Here, we demonstrate the first blind-NLE using affinity propagation (AP) clustering for single-channel and WDM CO-OFDM.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, full-step DBP (FS-DBP) is very complex and V-NLE shows marginal performance enhancement accompanied with a significant amount of floating-point operations, thus forbidding their implementation in real-time communications. Moreover, albeit the Kerr-induced nonlinear process is deterministic, in multicarrier schemes like coherent optical OFDM (CO-OFDM) the resulting nonlinear interaction between subcarriers becomes very complicated appearing random due to its high peak-toaverage power ratio (PAPR) [3]. Recently, unsupervised and supervised machine learning such as K-means clustering [4] and artificial neural network classification [3] have been introduced in optical communications to combat stochastic source of noises, performing blind and non-blind NLE, respectively.…”

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confidence: 99%

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Affinity propagation clustering for blind nonlinearity compensation in coherent optical OFDM

Aldaya

Wei

Sánchez

et al. 2018

Conference on Lasers and Electro-Optics

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Abstract:The first blind nonlinear equalizer using affinity propagation (AP) clustering is experimentally demonstrated for single-channel and WDM CO-OFDM. AP outperforms fuzzylogic c-means clustering and digital-back propagation for both QPSK and 16-QAM formats. IntroductionEndeavors to surpass the Kerr nonlinearity limit in long-haul coherent communications have been attempted in digital domain by Volterra-based nonlinear equalization (V-NLE) [1] and digital-back propagation (DBP) [2]. V-NLE and DBP however, can only tackle deterministic nonlinearities such as self-phase modulation, without considering the stochastic nonlinear interaction from polarization-mode dispersion and amplified spontaneous emission noise caused by cascaded optical amplifiers. On the other hand, full-step DBP (FS-DBP) is very complex and V-NLE shows marginal performance enhancement accompanied with a significant amount of floating-point operations, thus forbidding their implementation in real-time communications. Moreover, albeit the Kerr-induced nonlinear process is deterministic, in multicarrier schemes like coherent optical OFDM (CO-OFDM) the resulting nonlinear interaction between subcarriers becomes very complicated appearing random due to its high peak-toaverage power ratio (PAPR) [3]. Recently, unsupervised and supervised machine learning such as K-means clustering [4] and artificial neural network classification [3] have been introduced in optical communications to combat stochastic source of noises, performing blind and non-blind NLE, respectively. Here, we demonstrate the first blind-NLE using affinity propagation (AP) clustering for single-channel and WDM CO-OFDM. AP outperforms fuzzy-logic C-means (FL) and K-means clustering, as well as digital deterministic solutions such as FS-DBP and V-NLE, by reducing a significant amount of stochastic nonlinear noise on middle subcarriers.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Affinity propagation clustering for blind nonlinearity compensation in coherent optical OFDM

Aldaya

Wei

Sánchez

et al. 2018

Conference on Lasers and Electro-Optics

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“…In optical communication systems, nonlinear distortion compensation using NNs has been conventionally studied for Intensity Modulation-Direct Detection (IM-DD) transmission systems [6,7]. Recently, nonlinear equalization using NNs in frequency domain was investigated for coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission systems, where many sub-NNs were used for subcarriers [8,9]. We proposed a novel nonlinear equalization method using an NN to compensate optical multi-level signals distorted by SPM [10].…”

Section: Introductionmentioning

confidence: 99%

Experimental demonstration of SPM compensation based on digital signal processing using a three-layer neural-network for 40-Gbit/s optical 16QAM signal

et al. 2018

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We experimentally demonstrated a novel nonlinearity mitigation scheme based on digital signal processing using a three-layer neural network (NN). 40-Gbit/s optical 16QAM signal distorted by SPM was compensated, improving EVM values by about 15%. We also performed numerical simulation of the proposed scheme, and confirmed that the experiment agrees with the results of the simulation. We performed 100 times of learning processes to find weight and bias of each neuron. However, we did not observe any serious local minimum. We also investigated the effect of the number of neurons of the NN on the compensation performance. Keywords: digital signal processing, nonlinear distortion, SPM, neural network Classification: Fiber-Optic Transmission for Communications , vol. 46, no. 16, pp. 1140-1141, Aug. 2010. DOI:10.1049/el.2010.1444 L. Liu, L. Li, Y. Huang, K. Cui, Q. Xiong, and F. N. Hauske, "Intrachannel nonlinearity compensation by inverse volterra series transfer function," J. References

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“…We studied SPM compensation schemes using NNs for multilevel modulation systems [11,12]. Recently, SPM compensation using NNs in frequency domain was investigated for coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission systems [13,14]. However, waveform distortions caused by XPM have not been taken into account in these studies.…”

Section: Introductionmentioning

confidence: 99%

XPM compensation in optical fiber transmission systems using neural-network-based digital signal processing

Owaki

Nakamura

2018

IEICE ComEX

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Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM

Cited by 167 publications

References 13 publications

Affinity propagation clustering for blind nonlinearity compensation in coherent optical OFDM

Affinity propagation clustering for blind nonlinearity compensation in coherent optical OFDM

Experimental demonstration of SPM compensation based on digital signal processing using a three-layer neural-network for 40-Gbit/s optical 16QAM signal

XPM compensation in optical fiber transmission systems using neural-network-based digital signal processing

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