Nonlinearity mitigation of optical Fast-OFDM signals using a Wiener-Hammerstein electrical equalizer

Mhatli, Sofien; Mallouki, Nasreddine; Ghanbarisabagh, Mohammad; Ammar, Mahmoud; Bouallègue, Ammar; Attia, Rabah

doi:10.1109/sai.2015.7237273

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…A Wiener-Hammerstein model (WH-NLE) can be represented through a finite impulse response (FIR) filter followed by a nonlinear polynomial filter, and then followed by a second FIR filter [77]. WH-NLE is characterized with fewer numbers of coefficients and it easier to implement but has a limited performance improvement of only 1 dB improvement in OSNR compared to conventional double side band (DSB) CO-OFDM at a BER of 10-e3 over 3500 km of SSMF length [82].…”

Section: Ann-nle Can Tackle Effectively Inter-channel and Intra-chann...mentioning

confidence: 99%

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

Mrabet

Dayoub

Belghith

2021

Trans Emerging Tel Tech

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In this survey, we analyze the newest machine learning (ML) techniques for optical orthogonal frequency division multiplexing (O-OFDM)-based optical communications. ML has been proposed to mitigate channel and transceiver imperfections. For instance, ML can improve the signal quality under low modulation extinction ratio or can tackle both determinist and stochastic-induced nonlinearities such as parametric noise amplification in long-haul transmission. The proposed ML algorithms for O-OFDM can in particularly tackle inter-subcarrier nonlinear effects such as four-wave mixing and cross-phase modulation. In essence, these ML techniques could be beneficial for any multi-carrier approach (e.g. filter bank modulation). Supervised and unsupervised ML techniques are analyzed in terms of both O-OFDM transmission performance and computational complexity for potential real-time implementation. We indicate the strict conditions under which a ML algorithm should perform classification, regression or clustering. The survey also discusses open research issues and future directions towards the ML implementation.

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Section: Ann-nle Can Tackle Effectively Inter-channel and Intra-chann...mentioning

confidence: 99%

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

Mrabet

Dayoub

Belghith

2021

Trans Emerging Tel Tech

Self Cite

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“…Due to the very low frequency spacing between sub-carriers, Fast-OFDM signals suffer more from inter-carrier interference compared to the conventional O-OFDM [3,15], consequently, the importance of realizing an equalizer to mitigate nonlinear impairments in optical Fast-OFDM is much higher. An attempt to tackle nonlinearities in optical Fast-OFDM systems was made via a Wiener-Hammerstein electrical equalizer, with however marginal performance improvement [20]. Unfortunately, the use of ANN nor IVSTF has not been reported yet, therefore in this work, for the first time, a low-complex ANN-based machine learning NLE is numerically demonstrated in low-cost intensity-modulated and directed-detected optical Fast-OFDM links using a standard single-mode fiber (SMF) as a transmission medium.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals

Jarajreh¹

2019

Applied Sciences

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Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer.

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IM/DD-OFDM Modem Based on Adaptive Equalization

Mhatli

Aldalbahi

Jasim

2019

2019 2nd International Conference on Computer Applications &Amp; Information Security (ICCAIS)

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Nonlinearity mitigation of optical Fast-OFDM signals using a Wiener-Hammerstein electrical equalizer

Cited by 4 publications

References 16 publications

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals

IM/DD-OFDM Modem Based on Adaptive Equalization

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