Stochastic Backpropagation for Coherent Optical Communications

Jiang, Nan; Gong, Yuan; Karout, Johnny; Wymeersch, Henk; Johannisson, Pontus; Karlsson, Magnus; Agrell, Erik; Andrekson, Peter A.

doi:10.1364/ecoc.2011.we.10.p1.81

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…To deal with stochastic disturbances, Bayesian detection theory can be used to formulate maximum a posteriori (MAP) detectors, which are provably optimal in terms of error probability. MAP detectors have been proposed for the discrete memoryless channel [22] assuming perfect CD compensation by inline optical dispersion compensators, and utilizing a look-up table detector that can mitigate data pattern dependent nonlinear impairments [23]. A low-complexity Viterbi detector is suggested as an alternative or complementing DBP for combating fiber nonlinearities [24].…”

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confidence: 99%

Stochastic Digital Backpropagation

Irukulapati

Wymeersch

Johannisson

et al. 2014

IEEE Trans. Commun.

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In this paper, we propose a novel detector for singlechannel long-haul coherent optical communications, termed stochastic digital backpropagation (SDBP), which takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. We discuss the design approach behind this detector, which is based on the maximum a posteriori (MAP) principle. As closed-form expressions of the MAP detector are not tractable for coherent optical transmission, we employ the framework of Bayesian graphical models, which allows a numerical evaluation of the proposed detector. Through simulations, we observe that by accounting for nonlinear signal-noise interactions, we achieve a significant improvement in system reach with SDBP over digital backpropagation (DBP) for systems with periodic inline optical dispersion compensation. In uncompensated links with high symbol rates, the performance difference in terms of system reach for SDBP over DBP is small. In the absence of noise, the proposed detector is equivalent to the well-known DBP detector.

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confidence: 99%

Stochastic Digital Backpropagation

Irukulapati

Wymeersch

Johannisson

et al. 2014

IEEE Trans. Commun.

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“…In our simulation, they are C ¼ 3 and ¼ 2. Moreover, we compare the M-ary SVM scheme with amplitude-dependent phase rotations at the transmitter and the receiver (we denote it as PR-Tx/Rx) [9], and the maximum likelihood with back rotation (this method is also introduced as reference scheme by [10], [11], and we denote it as ML-BR). According to the technique of PR-Tx/Rx scheme [9], the transmitted signal phase is firstly prerotated by…”

Section: Resultsmentioning

confidence: 99%

“…Following the model of [8]- [11], in our simulation, the transmission link consists of N Â 80 km dispersion-shifted fiber (DSF) spans. The fiber loss coefficient is 0.2 dB/km, corresponding to a 16 dB loss of each span.…”

Section: Simulation System Descriptionmentioning

confidence: 99%

“…The amplitude-dependent phase rotation for transmitted or received signals can reduce the NLPN variance effectively [8], [9]. Based on constructing the likelihood function, a close-form maximum likelihood-based data detection algorithm was proposed [10], as well as a maximum a posteriori detector based on the Bayesian framework of factor graphs [11]. However, because of the stochastic nature and nonlinear properties of the NLPN, above approaches to suppress NLPN are all depends on the parameters of the transmission link.…”

Section: Introductionmentioning

confidence: 99%

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Nonparameter Nonlinear Phase Noise Mitigation by Using M-ary Support Vector Machine for Coherent Optical Systems

et al. 2013

IEEE Photonics J.

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The M-ary support vector machine (SVM) is introduced as a nonparameter nonlinear phase noise (NLPN) mitigation approach for the coherent optical systems. The NLPN tolerance of the system can be improved by using the M-ary SVM to conduct nonlinear detection. In this scheme, SVMs with different classification strategies are utilized to execute binary classification for signals impaired by fiber NLPN. Since the separating hyperplane of each SVM is constructed by training data, this scheme is independent from the knowledge of the transmission link. In numerical simulation, the M-ary SVM performs better than the method of amplitude-dependent phase rotation at the transmitter and receiver, as well as the maximum likelihood detection with back rotation.Index Terms: Nonlinear phase noise, quadratic amplitude modulation (QAM), support vector machine (SVM), M-ary SVM.

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“…Recently, RL-SARSA based ML method has been evaluated in [11][12] while SVM and KNN based ML methods have been utilized too in [13][14][15]. SBP method has also been evaluated for reducing the impairments in optical communication systems [16]. The other important application of ML is monitoring the performance of optical communication system.…”

Section: Machine Learning Applications In Optical Communication Smentioning

confidence: 99%

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

Hadi¹

2019

Preprint

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Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

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Stochastic Backpropagation for Coherent Optical Communications

Abstract: Abstract:We present stochastic backpropagation, a novel maximum a posteriori detection method for coherent optical communications. The proposed detector is shown to outperform conventional backpropagation in a scenario where nonlinear phase noise is the dominant impairment.

Cited by 8 publications

References 14 publications

Stochastic Digital Backpropagation

Stochastic Digital Backpropagation

Nonparameter Nonlinear Phase Noise Mitigation by Using M-ary Support Vector Machine for Coherent Optical Systems

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

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