Noise Analysis of Zero-Forcing Nonlinear Equalizers for Coherent WDM Systems

Ghazisaeidi, Amirhossein

doi:10.1109/jlt.2019.2900918

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…Hence, the stochastic NLI produces distortions with random and timevarying nature, restricting the strategies that the receiver can adopt to compensate for it. The majority of the NLC methods aim for compensating the deterministic fraction of the distortions via zero-forcing equalization [7], while only the slow, time-varying component of the stochastic NLI can be targeted by adaptive equalization [6]. The stochastic NLI is considered to be the ultimate impairment limiting the rates over the optical fiber channel.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Turbo Equalization for Nonlinearity Compensation in WDM Systems

Silva¹,

Yankov²

2021

Preprint

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In this paper, the performance of adaptive turbo equalization for nonlinearity compensation (NLC) is investigated. A turbo equalization scheme is proposed where a recursive leastsquares (RLS) algorithm is used as an adaptive channel estimator to track the time-varying intersymbol interference (ISI) coefficients associated with inter-channel nonlinear interference (NLI) model. The estimated channel coefficients are used by a MIMO 2 × 2 soft-input soft-output (SISO) linear minimum mean square error (LMMSE) equalizer to compensate for the time-varying ISI. The SISO LMMSE equalizer and the SISO forward error correction (FEC) decoder exchange extrinsic information in every turbo iteration, allowing the receiver to improve the performance of the channel estimation and the equalization, achieving lower bit-error-rate (BER) values. The proposed scheme is investigated for polarization multiplexed 64QAM and 256QAM, although it applies to any proper modulation format. Extensive numerical results are presented. It is shown that the scheme allows up to 0.7 dB extra gain in effectively received signal-to-noise ratio (SNR) and up to 0.2 bits/symbol/pol in generalized mutual information (GMI), on top of the gain provided by single-channel digital backpropagation.

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Section: Introductionmentioning

confidence: 99%