Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Abstract

Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Future Internet 2019, 11, 2 2 of 20 between the spatial modes, compared to the SMFs. On the other hand, the drive towards higher-order modulation formats, such as 16-QAM, and spectral-efficient techniques, such as orthogonal frequency division multiplexing (OFDM), lead to greater transmission impairments, reducing the maximum distance over which increased capacity can be provided. More specific, denser constellation diagrams render higher-order modulation formats are more susceptible to circularly-symmetric Gaussian noise as generated by Erbium-doped fiber amplifiers (EDFAs) along the transmission link [6]. Even though the launch power per wavelength channel can be increased to improve the signal-to-noise ratio (SNR) at the receiver, transmission is limited by nonlinear distortions due to the Kerr effect, which have a more severe impact on higher-order modulation formats and spectral-efficient modulation schemes [5,7].Moreover, the transmission of more than two signal wavelengths (wavelength-division multiplexing, WDM) through an optical fibre generates four-wave mixing (FWM), a process caused by the power dependence of the refractive index of the optical fibre [3]. FWM is related to fibre nonlinearity and gives rise to new wavelengths which significantly degrade the signal quality especially at high optical powers and when signals are spectrally close to each other. FWM is one of the most dominant nonlinear effects in optical networks and a primary root of the capacity crunch [3]. Since nonlinear noise such as FWM is highly correlated to signals themselves, nonlinearity can be mitigated by performing special treatment of the signals or conducting post-transmission digital signal processing (DSP) on received signals [5,7].On the other hand, coherent optical OFDM (CO-OFDM) [8] has attracted a lot of interest in optical fiber communications due to its simplified DSP units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio (PAPR) imposes high vulnerability to fiber-induced nonlinearities [8]. Attempts to combat nonlinearities in CO-OFDM have been performed by deterministic nonlinearity compensators which take advantage of the fact that light scattering within a fibre is a deterministic process. Key te...

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

“…When evaluating and comparing equalizers complexity it is essential to take the nature of the equalizer in to considerations, such as the DBP and IVSTF are essentially different from machine learning-based NLEs such as ANN and SVM. That is, as the IVSTF equalization mechanism is based on the idea of reversing of the effect propagation model; consequently, which makes it dependent on the number of fiber spans and the oversampling rate, therefore complexity, does not depend on other signal parameters such as modulation format levels [13].…”

“…In traditional O-OFDM, several digital signal processing (DSP) techniques for mitigation of fiber nonlinearities have been proposed, such as the digital back propagation (DBP) [8], the inverse Volterra-series transfer function (IVSTF) [9], and hybrid pre-and post-nonlinearity compensators [10,11]. In DBP the equalizer attempts to reverse the channel non-linear effects, where the SMF channel is thoroughly and extensively modelled, thereafter, the received signals are digitally back-propagated over the modelled channel with the help of the split-step Fourier (SSF) operations at a very small distances may be 40 FFT/IFFT operations per span; which makes this method impractical and computationally expensive for real-time applications as there are a huge number of computation steps [12,13]. On the other hand, The IVSTF algorithm was presented in order to reduce the complexity of digital back-propagation (DBP); this removed the necessity for the split-step Fourier (SSF) method, which is computationally incompetent.…”

“…On the other hand, The IVSTF algorithm was presented in order to reduce the complexity of digital back-propagation (DBP); this removed the necessity for the split-step Fourier (SSF) method, which is computationally incompetent. The VSTF offers an analytical tool for expressing the fiber non-linear effects by similarly constructing the inverse channel based on IVSTF depending upon the number of fiber spans not the fiber length as in DBP; which significantly reduces computational complexity compared to DBP [12][13][14][15]. Moreover, IVSTF has shown marginal signal quality-factor improvement for coherent O-OFDM using 16-quadrature amplitude modulated (16-QAM) sub-carriers but at lower DSP computational effort [11].…”

Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes

“…To this end, orthogonal frequency division multiplexing (OFDM)-based radio-over-fiber (RoF) technology, combining the benefits of optical fiber and millimeter-wave wireless links, has been proven as the solution to support secure, cost-effective, and high-capacity vehicular/mobile/wireless access for the topic generation communication systems [2]. There are two types of receiver used in the implementation of RoF-OFDM systems: direct detection [3] and coherent detection [4]. Direct detection is characterized by a simple and economical base station, but with the cost of a worse receiving sensitivity when contrasted with coherent detection.…”

Histogram Based Clustering for Nonlinear Compensation in Long Reach Coherent Passive Optical Networks

“…In this scenario, machine learning emerges as a high potential set of tools to analyse and process complex systems where analytical modelling is unfeasible or the computational cost to solve it is excessively high [20]. Thus, several groups have proposed different machine learning based approaches to overcome the degrading effect of nonlinear distortion in fibre communication systems [21][22][23][24]. In [25][26][27], artificial neural networks were employed, whereas in [28] and in [29,30], support vector machines (SVMs) were proposed.…”