Predicting the Quality of Transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (say, e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for lightpath QoT belong to two categories: "exact" analytical models estimating physical layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas which are computationally faster, but typically introduce high link margins that lead to underutilization of network resources. In this paper we explore a third option, i.e., Machine Learning (ML), as ML techniques have been already successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit-error rate of unestablished lightpaths meets the required system threshold, based on traffic volume, desired route and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.

The information rate transferred through the Additive\ud White Gaussian Noise (AWGN) channel affected by discrete-time\ud multiplicative Wiener’s phase noise is computed in the paper.\ud Upper and lower bounds based on quantization of the phase space\ud and trellis representation of phase noise memory are proposed. The\ud results presented in the paper show that both the upper bound and\ud the lower bound converge to the actual information rate as the\ud number of quantizer’s bins increases, the convergence of the lower\ud bound being faster. The analysis presented in the paper allows for\ud pilot symbols being considered. From the one hand, the insertion of\ud pilot symbols penalizes the net symbol rate, thus subtracting space\ud to information transmission, while, from the other hand, they bring\ud information on channel state. The balance between what is lost and\ud what is gained is computed in the paper

No abstract

The capacity of a Multiple-Input Multiple-Output (MIMO) channel in which the antenna outputs are processed by an analog linear combining network and quantized by a set of threshold quantizers is studied. The linear combining weights and quantization thresholds are selected from a set of possible configurations as a function of the channel matrix. The possible configurations of the combining network model specific analog receiver architectures, such as single antenna selection, sign quantization of the antenna outputs or linear processing of the outputs. An interesting connection between the capacity of this channel and a constrained sphere packing problem in which unit spheres are packed in a hyperplane arrangement is shown. From a high-level perspective, this follows from the fact that each threshold quantizer can be viewed as a hyperplane partitioning the transmitter signal space. Accordingly, the output of the set of quantizers corresponds to the possible regions induced by the hyperplane arrangement corresponding to the channel realization and receiver configuration. This connection provides a number of important insights into the design of quantization architectures for MIMO receivers; for instance, it shows that for a given number of quantizers, choosing configurations which induce a larger number of partitions can lead to higher rates. 1

The constrained capacity of the additive white Gaussian noise channel affected by Wiener phase noise is investigated in the letter. Evaluation of channel capacity based on quantization of the phase space and trellis representation of the memory is proposed. The results presented in the letter show that quantization of the phase into 128 bins is adequate to accurately evaluate the capacity at signal-to-noise ratio lower than 30 dB.