We present the results of the comparative performance-versus-complexity analysis for the several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison is carried out using an experimental set-up with the transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multilayer perceptron. Their performance is compared with the one delivered by the previously proposed NN-based equalizers: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. First, we present the general expressions for the computational complexity associated with each NN type; these are given in terms of real multiplications per symbol. We demonstrate that in the experimental system considered, the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the largest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). Then, we examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to some lower levels, the three-layer perceptron provides the best performance.
Fiber-optic multi-band transmission (MBT) aims at exploiting the low-loss spectral windows of single-mode fibers (SMFs) for data transport, expanding by ∼11× the available bandwidth of C-band line systems and by ∼5× C+L-band line systems'. MBT offers a high potential for cost-efficient throughput upgrades of optical networks, even in absence of available dark-fibers, as it utilizes more efficiently the existing infrastructures. This represents the main advantage compared to approaches such as multi-mode/-core fibers or spatial division multiplexing. Furthermore, the industrial trend is clear: the first commercial C+L-band systems are entering the market and research has moved toward the neighboring S-band. This article discusses the potential and challenges of MBT covering the ITU-T optical bands O → L. MBT performance is assessed by addressing the generalized SNR (GSNR) including both the linear and non-linear fiber propagation effects. Non-linear fiber propagation is taken into account by computing the generated non-linear interference by using the generalized Gaussian-noise (GGN) model, which takes into account the interaction of nonlinear fiber propagation with stimulated Raman scattering (SRS), and in general considers wavelength-dependent fiber parameters. For linear effects, we hypothesize typical components' figures and discussion on components' limitations, such as transceivers', amplifiers' and filters' are not part of this work. We focus on assessing the transmission throughput that is realistic to achieve by using feasible multi-band components without specific optimizations and implementation discussion. So, results are meant to address the potential throughput scaling by turningon excess fiber transmission bands. As transmission fiber, we focus exclusively on the ITU-T G.652.D, since it is the most widely deployed fiber type worldwide and the mostly suitable to multi-band transmission, thanks to its ultra-wide low-loss singlemode high-dispersion spectral region. Similar analyses could be This work was fundedby the H2020 Metro-Haul project, no. 761727; and by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie ETN WON, grant agreements 814276.
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The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented.
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