I Introduction
Machine learning techniques[1, 2, 3] have been recently applied to optical communications systems to deal with various issues such as network monitoring[4, 5, 6], traffic control[7, 8, 9, 10], signal design[11, 12, 13, 14, 15], and nonlinearity compensation[16, 17, 18, 19, 20, 21]. Since the fiber nonlinearity is a major limiting factor to the achievable information rates[22, 23, 24], mitigating nonlinearity has been of great importance to realize highspeed, reliable, and longreach optical communications. Conventionally, a number of modelbased nonlinear equalizers to compensate for fiber distortion were investigated, e.g., maximumlikelihood sequence equalizer (MLSE)[25, 26, 27], turbo equalizer (TEQ) [28, 29, 30], Volterra series transfer function (VSTF) [33, 32]
, and digital backpropagation (DBP)
[35, 38, 37, 36]. However, those nonlinear equalizations are computationally complex and susceptive to model parameter mismatch in general. Recent datadriven approaches motivated by deep learning can favorably replace such traditional modelbased methods as the use of deep neural networks (DNN) allows flexible statistical analysis of complicated fiberoptic systems without relying on specific models. In the past few years, DNN has shown its high potential in nonlinear performance improvement, e.g., [16, 17, 18, 19, 20, 21, 12, 13, 14, 15].Nonetheless, most existing work did not appropriately account for practical interaction with forward error correction (FEC) codes. For example, multiclass softmax crossentropy loss is often used to train DNN, which is relevant only when nonbinary FEC codes are assumed. For more practical bitinterleaved coded modulation (BICM) systems, it was found in [20] that binary crossentropy (BCE) loss can improve accuracy and scalability to highorder quadratureamplitude modulation (QAM). In this paper, we propose a novel DNN application to perform TEQ for nonlinear mitigation in the context of BICM with iterative demodulation (ID). Although DNN has already been popular in nonlinear compensation, our paper is the first attempt to adopt DNN for TEQ in the framework of BICMID which takes softdecision feedback from the FEC decoder to refine the DNN output for improved equalization accuracy. We make an analysis of the extrinsic information transfer (EXIT) of turbo DNN, and demonstrate that the proposed DNN paired with irregular lowdensity paritycheck (LDPC) codes used in DVBS2 standards offers a significant performance gain by accelerating the decoder convergence in nonlinear transmissions.
The contributions of this paper are summarized as follows:

Trend overview: We first overview the recent trend of deep learning in optical society.

Multilabel DNN: We then verify that nonbinary crossentropy is not scalable to highorder QAM signals and DNN trained with BCE loss can appropriately compensate for fiber nonlinearity.

Turbo DNN: We propose a nested residual DNN architecture for TEQ to further improve performance.

EXIT analysis: We analyze EXIT chart of our DNNTEQ and show that DNNTEQ accelerates decoding convergence.

LDPC design: We optimize degree distribution of LDPC codes to match EXIT charts of DNNTEQ, achieving higher throughput.
Note that due to the above contributions, in particular the demonstration of rate improvement with optimized LDPC codes for DNNTEQ, this paper is distinguished from our preliminary reports[48, 20, 21]. To the best of authors’ knowledge, there is no other literature which applied DNN to TEQ for nonlinear compensation.
Ii Machine Learning for Optical Communications
Iia Trend Overview
Fiberoptic communications suffer from various linear and nonlinear impairments, such as laser linewidth, amplified spontaneous emission (ASE) noise, chromatic dispersion (CD), polarization mode dispersion (PMD), selfphase modulation (SPM), crossphase modulation (XPM), fourwave mixing (FWM), and crosspolarization modulation (XPolM)[22, 23, 24]. Although the physics is well governed by nonlinear Schrödinger equation (NLSE) model, we may need highcomplexity splitstep Fourier method (SSFM) to solve lightwave propagation numerically. It is hence natural to admit that the nonlinear physics necessitates nonlinear signal processing to appropriately deal with the nonlinear distortions in practice.
In place of conventional modelbased nonlinear signal processing, the application of machine learning techniques[1, 2, 3] to optical communication systems has recently received increased attention[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. The promise of such datadriven approaches is that learning a blackbox DNN could potentially overcome situations where limited models are inaccurate and complex theory is computationally intractable.
Fig. 1 shows the trend of machine learning applications in optical communications society in the past two decades. Here, we plot the number of articles in each year according to Google Scholar search of the keyword combinations; “machine learning” + “optical communication” or “deep learning” + “optical communication.” As we can see, machine learning has been already used for optical communications since twenty years ago. Interestingly, we discovered the Moore’s law in which the number of applications exponentially grows by a factor of nearly per year. For deep learning applications, more rapid annual increase by a factor of can be found in the past half decade. As of today, there are nearly thousand articles of deep learning applications. Note that the author’s article[48] in 2014 is one of very first papers discussing the application of deep learning to optical communications.
IiB Statistical Learning Techniques
We briefly overview some learning techniques to analyze nonlinear statistics applied to optical communications as shown in Fig. 2
. For example, density estimation trees (DET), kernel density estimation (KDE) and Gaussian mixture model (GMM) can be alternative to histogram analysis. Principal component analysis (PCA) and independent component analysis (ICA) are useful to analyze important factors of data. For highdimensional data sets, we may use Markovchain Monte–Carlo (MCMC) and importance sampling (IS). To analyze stochastic sequence data, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) based on hidden Markov model (HMM) may be used.
Since mid70’s, artificial neural networks (ANN) have led machine learning researches. Various topology including multilayer perceptron (MLP), Hopfield neural networks (HNN), restricted Boltzmann machines (RBM), convolutional neural networks (CNN), and recurrent neural networks (RNN) have been investigated. Since mid90’s, support vector machine (SVM) has taken over the lead for machine learning. One of important techniques to analyze nonlinear statistics is kernel trick, in which we analyze higherdimensional linearlized feature spaces called reproducing kernel Hilbert space (RKHS) with kernel functions including radial basis function (RBF). Since 2006, deep learning
[1]based on DNN has been a major breakthrough in media signal processing fields. In deep learning, manylayer deep belief networks (DBN) is trained with a massively large amount of datasets.
IiC Classic Machine Learning Applications
Now, we show a few examples of machine learning approaches applied to nonlinear fiberoptic communications. Xie et al. proposed the use of ICA for polarization recovery[39] as an alternative to constantmodulus adaptation (CMA). Shallow ANNbased nonlinear equalizers have been studied in literature[40, 41, 42]. We have investigated GMMbased sliding MLSE and TEQ receivers[27], where upto dB performance improvement was achieved compared to DBP. SVM has been also studied as another nonlinear equalizer[43, 44], in which a complicated decision rule like Yin–Yang spiral boundary[45] can be learned by kernelSVM. RBF kernels have been studied in other literature, e.g., [46]. HMMbased turbo cycleslip recovery[47] offers greater than dB gain. A stochastic DBP proposed in [38] exhibits an outstanding performance by solving inverse NLSE with SSFM, which adopts MCMC particle representation of stochastic noise.
IiD Modern Deep Learning Applications
As shown in Fig. 1
, there exist a lot of deep learning applications, among which a limited number of examples are listed below. DNN was introduced for optical signaltonoise ratio (OSNR) monitoring in
[4]. Modulation classification as well as OSNR monitoring was considered in [5], and a deep CNN showed an accurate performance in [6]. Deep learningbased network management and resource allocation were studied in [7] and [8]. Analogously, traffic optimization based on deep reinforcement learning (DRL) was also considered in
[9, 10]. Various endtoend deep learning which jointly optimizes signal constellation and detection have been proposed, e.g., [11, 12, 13, 14, 15], where denoising autoencoder (AE) architecture is trained through nonlinear fiber channels. Also for receiverend design, many DNN equalizers to compensate for fiber nonlinearity were introduced for coherent or noncoherent optical links, e.g., [16, 17, 18, 19, 20, 21].Note that big data necessary for deep learning are readily available in highspeed optical communications, where we can obtain gigabits or terabits of data in a second[51]
. In addition, the DNN is massively parallelizable in hardware implementation, which is suited for future optical communications. In modern DNN, various techniques have been introduced, e.g., pretraining, minibatch, rectified linear unit (ReLU), dropout, batch normalization, skip connection, inception, adaptivemomentum (Adam) stochastic gradient, adversarial, and long shortterm memory (LSTM) architectures
[3].Iii Deep Learning for Nonlinear Compensation
Similar to the other DNN equalizers, we focus on deep learning for fiber nonlinearity compensation. This paper has a distinguished contribution over existing literature as we propose a novel DNNbased TEQ suited for BICMID systems where stateoftheart LDPC codes are employed.
Iiia Nonlinear FiberOptic Communications System
The optical communications system under consideration is depicted in Fig. 3. Threechannel DPQAM signals for GBd baud rate and GHz channel spacing are sent over fiber plants towards coherent receivers. We consider spans of dispersion managed (DM) links with km nonzero dispersionshifted fiber (NZDSF) at a residual dispersion per span (RDPS) of %. The NZDSF has a dispersion parameter of ps/nm/km, a nonlinear factor of /W/km, and an attenuation of dB/km. The span loss is compensated by Erbiumdoped fiber amplifiers (EDFA) with all ASE noise added just before the receiver assuming the noise figure of dB. We use digital rootraised cosine filters with % rolloff at both transmitter and receiver. The receiver employs standard phase recovery and linear equalization (LE) to compensate for linear dispersion. Due to fiber nonlinearity, residual distortion after LE will limit the achievable information rates.
Fig. 4 shows an example of residual distortion of DP16QAM constellation after tap leastsquares LE for span transmissions. We can see that the constellation is more seriously distorted with the increased launch power due to Kerr fiber nonlinearity. To compensate for the residual nonlinear distortion, we introduce DNNbased TEQ, which exploits softdecision feedback from FEC decoder as shown in Fig. 3.
IiiB Scalable Deep Neural Network Equalization
Before introducing DNNTEQ, we discuss loss function to train DNN equalizers suited for BICM. Consider DP16QAM equalization, where there are
bits per symbol, leading to classes to identify. For such multiclass learning, we may use a single nonbinary softmax classification shown in Fig. 5(LABEL:sub@dnn_single), analogous to [16]. However, this nonbinary (NB) DNN does not perform well for higherorder DPQAM in particular for a limited number of training data. For example, DP64QAM requires classes to identify per symbol, which necessitates unrealistically huge data sets for training.To be scalable in highorder QAM, we shall use multilabel classification which employs multiple BCE losses as shown in Fig. 5(LABEL:sub@dnn_multi). The multilabel DNN produces loglikelihood ratio (LLR), which can be directly fed into SDFEC decoder without external processing such as [16, 49]. This is a great advantage in practice because LLR calculation is cumbersome, especially for highorder and highdimensional modulation. Note that sum of crossentropy minimization is equivalent to maximizing the lower bound of generalized mutual information (GMI), which is used for SDFEC performance metric.
IiiC Nonbinary vs. Binary DNN Equalization
We compare DNN and LSTM with classical machine learning methods, specifically, linear discriminant analysis (LDA), naïve Bayes (NB), quadratic discriminant analysis (QDA), and SVM. For multiclass SVM, we use onevsone rule with linear kernel as it worked best among several variants such as onevsall and polynomial kernel. The DNN weight is trained by Adam with a dropout ratio of and a batch size of symbols to minimize a sum of softmax crossentropy loss across all labels, using approximately training symbols. Figs. 6, 7, and 8 show the Q factor versus launch power of DP4QAM, DP16QAM, and DP64QAM, respectively, for , , and spans times km fiber configurations. It is observed that DNN can offer the best performance among other methods, achieving greater than dB gain over LE in highly nonlinear regimes. More importantly, the conventional DNN with nonbinary softmax crossentropy does not perform well for highorder QAMs. It suggests that DNN equalizers using BCE loss function has a great advantage not only for BICM compatibility but also for highorder QAM scalability.
Iv Neural Turbo Equalization: DNNTEQ
Iva Nested Residual Network Architecture
Fig. 9 shows the architecture of our turbo DNN equalizer, which feeds distorted DPQAM signals over consecutive tap symbols to generate softdecision LLR values for FEC decoding. The major extension from conventional DNN lies in the input layer which takes a priori (APR) side information along with DPQAM symbols. The APR side information comes from FEC decoder representing intermediate softdecision LLRs in run time. For efficient DNN training, the APR values having mutual information of
are synthetically generated via a Gaussian distribution following
where is an original bit and with being ten Brink’s Jinverse function [52], instead of considering a particular FEC decoder feedback.The last layer has two branches, i.e., extrinsic (EXT) output and a posterioriprobability (APP) output, which uses a skip connection from the input layer to sum up EXT and APR at a target symbol. This nested residual network tries to train extrinsic message passing for TEQ realization. It was found that learning DNN model to minimize APP crossentropy loss does not always minimize EXT crossentropy loss accordingly, and vice versa. In order to keep both APP and EXT outputs reliable, we use a maxpooling layer following sigmoid crossentropy loss.
The DNN uses four hidden layers, each of which consists of batch normalization, ReLU activation, and a fullyconnected linear layer with skip connections and % dropout for neuron nodes. The DNN is trained with Adam for a minibatch size of symbols to minimize the worst sigmoid crossentropy losses between APP and EXT outputs, using training datasets of approximately symbols. An early stopping with a patience of is carried out up to a maximum of epochs.
IvB EXIT Chart Analysis
Fig. 10 shows the EXIT chart of DNNTEQ given LLRs having a certain mutual information from the FEC decoder. It is clearly observed that the DNN outputs can be greatly improved by feeding in the FEC softdecision. An almost linear slope towards in EXIT curve is achieved, implying that crossentropy loss is mitigated linearly with FEC feedback reliability. This steep slope in the EXIT curve of DNNTEQ can eventually make a significant improvement in LDPC decoding performance, as shown in Fig. 11, where we present the decoding trajectory between the variablenode decoder (VND) and the checknode decoder (CND) in the LDPC decoder. Here, we use a combined EXIT chart [52] of DNNTEQ and LDPC decoder, for DP16QAM 16span DM links at dBm launch power and DVBS2 LDPC codes with a code rate of . As shown, the conventional DNN equalizer without FEC feedback requires a large number of decoder iterations to reach an errorfree mutual information of . Whereas for DNNTEQ, we can open up an EXIT tunnel between VND and CND curves, that leads to a considerable acceleration of the decoder convergence to reach errorfree condition within only a few iterations.
IvC BER Performance
We assume the use of an outer Bose–Chaudhuri–Hocquenghem (BCH) code with a rate of [51], having a minimum Hamming distance of . Based on the union (upper) bound, the biterror rate (BER) threshold for this outer BCH code is at or above an input BER of to achieve an output BER below . Hence, a postLDPC BER below can be successfully decoded to a BER below when this outer BCH code is used.
For FEC codes, we consider variablerate irregular LDPC codes of block length bits, used in DVBS2 standards. The LDPC codes have a different degree distribution for individual code rates. For instance at a code rate of , the variable degree polynomial (node perspective) is given as , whereas the check degree polynomial is . At a code rate of , the variable and check degree polynomials are and , respectively. We also consider an optimized degree distribution for DNNTEQ as done analogously in [52], where the EXIT chart of DNNTEQ in Fig. 10
is modeled with cubic functions and EXIT curves of combined VND and DNNTEQ are optimized for tripledegree checkconcentrated distribution, which has two degrees of freedom to search for the best distribution. For example, the optimized LDPC code for a code rate of
at a launch power of dBm for DP64QAM systems has a degree distribution of .Figs. 12 and 13 show the postLDPC BER performance versus launch power of DP16QAM and DP64QAM, respectively, for , and spans of NZDSF links. We compare DVBS2 LDPC codes for LE, DNN and DNNTEQ and our optimized LDPC code for DNNTEQ. From the figures, we can observe the following results:

Although DNN nonlinear compensation can improve BER performance of LE, achieving a BER of BCH threshold is mostly in failure.

DNNTEQ can significantly improve the BER performance of DNN to reach the threshold and about dB margin around optimal launch power is realized.

Optimizing LDPC codes for DNNTEQ can offer an additional marginal improvement over the standard DVBS2 LDPC codes for the whole range of launch power.
IvD Achievable Rate Performance
The BER improvement with our proposed DNNTEQ implies that we can increase the achievable throughput when the code rate is adaptively optimized. Fig. 14 shows achievable rate performance for DP64QAM at span NZDSF links. Here, we use the same variable node degree of DVBS2 rate and plot the largest code rate such that the postLDPC BER meets the BCH threshold by varying the check node degree to be a target rate. From this figure, we can see that the DNN nonlinear compensation can improve the performance of LE by b/s/Hz in the nonlinear regimes, and the achieved gain in the peak throughput is about b/s/Hz. Our DNNTEQ offers a remarkable BICMID gain over the whole range of launch power, achieving a throughput improvement of b/s/Hz over the DNN when LDPC code is optimized. A total throughput improvement of b/s/Hz from the standard LE was achieved by the proposed DNNTEQ.
V Conclusions
We extended DNN machine learning techniques to TEQ for improved nonlinear compensation in coherent fiber communications. We first verified that DNN trained with binary crossentropy loss can outperform various machine learning techniques to compensate for fiber nonlinearity. Through EXIT chart analysis, we then confirmed that the proposed DNNTEQ offers decoder acceleration by feeding intermediate softdecision LLR from the LDPC decoder. Our DNNTEQ significantly improves BER performance through the turbo iteration. We also investigated LDPC code design to match the EXIT chart of DNNTEQ, and demonstrated that the proposed DNNTEQ with optimized LDPC codes can improve the achievable throughput by b/s/Hz over linear equalization with standard LDPC codes. To the best of authors’ knowledge, this is the first paper investigating TEQ based on DNN for fiber nonlinearity mitigation.
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