Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation

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. 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

Learning for Perturbation-Based Fiber Nonlinearity Compensation

Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation triplets of PB-NLC is preferable over feedforward neural-network solutions

On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Detection

In this paper, we use a low-cost low-power mm-wave frequencymodulated continuous wave (FMCW) radar for in-vehicle occupantmonitoring. We propose an algorithm to identify occupied seats. In-stead of using a high-resolution radar which increases the cost andarea, we integrate machine learning algorithms with the results ofcovariance-based angle of arrival estimation capon beamformer

Model-based machine learning techniques for fiber nonlinearity compensation

The demand for faster high-volume data transmission over fiber optical long-haul links has been ever-increasing. This has been driving the need for further enhancing the data rates that can be carried by already deployed fibers. This is a non-trivial task as the Kerr effect causes nonlinear distortions to grow with increasing transmission power. Therefore, we are faced with the unusual situation that the effective signal-to-noise ratio decreases as the transmit power increases. Conventional nonlinear compensation methods, such as digital backpropagation (DBP) and perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over fibers. However, their performances are limited by model mismatch and computational complexity. Recently, machine learning (ML) techniques have been used for the optimization of parameters of model-based approaches, which traditionally have been determined analytically from physical models. In the context of optical fiber transmission, it has been shown that ML-aided model-based approaches have improved performance and/or reduced complexity. In this thesis, we consider two specific ML-aided model-based nonlinear compensation approaches: learned DBP (LDBP) and learned PB-NLC. In our first contribution, starting from LDBP proposed in the existing literature, we propose a novel perturbation theory-aided learned digital backpropagation method. The key insight is that the number of steps of LDBP can significantly be decreased by augmenting each step with a filter response, as suggested by perturbation theory. We demonstrate that our proposed approach outperforms existing LDBP in terms of both performance and complexity. Our second contribution concerns the learned PB-NLC. We conduct a comprehensive performance-complexity analysis for various learned and non-learned PB-NLC approaches presented in the literature, utilizing state-of-the-art complexity reduction methods to map out the performance-complexity trade-off among them. Our results show that least squares-based PB-NLC with clustering quantization has the best performance-complexity trade-off. We advance the state-of-the-art of learned PB-NLC by developing a bi-directional recurrent neural network for generating features that are alike those obtained from perturbation theory and are used as input for learned nonlinearity compensation. We demonstrate that our proposed feature learning network achieves a similar performance as least-squares PB-NLC, but with a reduced complexity.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat