This paper proposes an Integer Linear Programming (ILP) framework to optimize the multi-period planning of metro-aggregation networks exploiting both point-to-multipoint coherent pluggable transceivers and a filterless line system architecture. We investigate several planning strategies to minimize cost while also considering traffic prediction and operational expenditures. The results of a reference network provide evidence of transceiver cost reductions between 20% and 32% compared to the point-to-point approach over a five-year planning period.

Emerging applications are driving traffic requirements to new heights, from IoT and autonomous driving to augmented reality and Industry 4.0. Urban areas, which have a significant fraction of the telecom infrastructure capital expenditures (CAPEX), are experiencing even more growth than other segments. Noteworthy, the traffic pattern in aggregation networks is hub-and-leaf, with only a few hub nodes communicating with multiple leaf nodes. Traditional point-to-point optical solutions are not the best fit for this pattern. A novel optical technology enabling point-to-multipoint operation has recently been proposed to address this limitation. Since this solution relies on broadcasting the optical signal over a virtual tree, its optimal deployment in generic fiber topologies (with failure survivability conditions) requires specific designs. This paper describes an integer linear programming model to optimize the design of survivable metro-aggregation networks exploiting point-to-multipoint coherent transceivers. Results obtained on a mesh network show evidence that transceiver expenditures can be reduced by a figure between 25% and 60%.

We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive. For the first time, we present possible drawbacks in using each type of predictive task in a machine learning context, considering the nonlinear coherent optical channel equalization/soft-demapping problem. We study two types of equalizers based on the feed-forward and recurrent NNs, for several transmission scenarios, in linear and nonlinear regimes of the optical channel. We point out that even though from the information theory perspective the cross-entropy loss (classification) is the most suitable option for our problem, the NN models based on the cross-entropy loss function can severely suffer from learning problems. The latter translates into the fact that regression-based learning is typically superior in terms of delivering higher Q-factor and achievable information rates. In short, we show by empirical evidence that loss functions based on cross-entropy may not be necessarily the most suitable option for training communication systems in practical scenarios when overfitting- and vanishing gradients-related problems come into play.

The ever-increasing traffic requirements need cost-efficient and reliable solutions. Metropolitan networks are responsible for a large fraction of the telecom infrastructure CAPEX. Albeit the traffic pattern in these networks tends to be hub-and-spoke, the current transceiver deployments do not leverage this feature. This paper describes and models various survivable metro-aggregation network scenarios using point-to-multipoint capable coherent transceivers. The results obtained over a reference mesh network show that these transceivers can reduce total transceiver costs by ~40% on average.

Addressing the optical communication systems employing the nonlinear Fourier transform (NFT) for the data modulation/demodulation, we provide an explicit proof for the properties of the signals emerging in the so-called b -modulation method, the nonlinear signal modulation technique that provides explicit control over the signal extent. We present details of the procedure and related rigorous mathematical proofs addressing the case where the time-domain profile corresponding to the b -modulated data has a limited duration, and when the bound states corresponding to specifically chosen discrete solitonic eigenvalues and norming constants, are also present. We also prove that the number of solitary modes that we can embed without violating the exact localisation of the time-domain profile, is actually infinite. Our theoretical findings are illustrated with numerical examples, where simple example waveforms are used for the b -coefficient, demonstrating the validity of the developed approach. We also demonstrate the influence of the bound states on the noise tolerance of the b-modulated system.

The channel law for amplitude-modulated solitons transmitted through a nonlinear optical fiber with ideal distributed amplification and a receiver based on the nonlinear Fourier transform is a noncentral chi-distribution with 2n degrees of freedom, where n = 2 and n = 3 correspond to the single- and dual-polarisation cases, respectively. In this paper, we study the capacity lower bounds of this channel under an average power constraint in bits per channel use. We develop an asymptotic semi-analytic approximation for a capacity lower bound for arbitrary n and a Rayleigh input distribution. It is shown that this lower bound grows logarithmically with signal-to-noise ratio (SNR), independently of the value of n. Numerical results for other continuous input distributions are also provided. A halfGaussian input distribution is shown to give larger rates than a Rayleigh input distribution for n = 1,2,3. At an SNR of 25 dB, the best lower bounds we developed are approximately 3.68 bit per channel use. The practically relevant case of amplitude shiftkeying (ASK) constellations is also numerically analyzed. For the same SNR of 25 dB, a 16-ASK constellation yields a rate of approximately 3.45 bit per channel use.