Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation

• Published in: Journal of lightwave technology Vol. 41; no. 12; pp. 3976 - 3985
• Main Authors: Luo, Shenghang, Soman, Sunish Kumar Orappanpara, Lampe, Lutz, Mitra, Jeebak
• Format: Journal Article
• Language: English
• Published: New York IEEE 15-06-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; Compensation; Complexity; Computation; Deep learning; Distortion; Exact solutions; Machine learning; Mathematical models; Neural networks; Nonlinear distortion; Nonlinearity; nonlinearity compensation; Optical communication; Optical fiber amplifiers; Optical fiber communication; Optical fiber networks; Optical fibers; Perturbation methods; Perturbation theory; perturbation theory-based nonlinearity compensation; Propagation; recurrent neural network; Recurrent neural networks; Symbols; Tradeoffs
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2023.3279449

Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over optical fibers. However, their practical usability is limited by model mismatch and the immense computational complexity associated with the analytical computation of perturbation triplets and the nonlinearity distortion field. Recently, machine learning techniques have been used to optimise parameters of PB-based approaches, which traditionally have been determined analytically from physical models. It has been claimed in the literature that the learned PB-NLC approaches have improved performance and/or reduced computational complexity over their non-learned counterparts. In this paper, we first revisit the acclaimed benefits of the learned PB-NLC approaches by carefully carrying out a comprehensive performance-complexity analysis utilizing state-of-the-art complexity reduction methods. Interestingly, our results show that least squares-based PB-NLC with clustering quantization has the best performance-complexity trade-off among the learned PB-NLC approaches. Second, we advance the state-of-the-art of learned PB-NLC by proposing and designing a fully learned structure by adopting the noiseless Manakov equation as the channel propagation model. We apply a bi-directional recurrent neural network for learning perturbation triplets that are alike those obtained from the analytical computation and are used as input features for the neural network to estimate the nonlinearity distortion field. Finally, we demonstrate through numerical simulations that our proposed fully learned approach achieves an improved performance-complexity trade-off compared to the existing learned and non-learned PB-NLC techniques.