Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …

This paper performs a detailed, multi-faceted analysis of key challenges and common
design caveats related to the development of efficient neural networks (NN) based nonlinear …

We present a novel autoencoder-based learning of joint geometric and probabilistic
constellation shaping for coded-modulation systems. It can maximize either the mutual …

Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance
optical fiber communication. Digital back-propagation (DBP) is a primary methodology to …

In this paper, the non-decomposition method is employed to investigate the quasi-projective
synchronization of fractional-order complex-valued neural networks (FOCVNNs) with …

Two conceptual convolutional neural network (CNN) schemes are proposed, developed and
analysed for directly decoding nonlinear frequency division multiplexing (NFDM) signals …

In this work, a perturbation-based neural network (P-NN) scheme with an embedded
bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the …

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 …

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is a
promising candidate for 5G mobile fronthaul system. Due to the inherent imaginary …

An entropy-regularized mean square error (MSE-X) cost function is proposed for nonlinear
equalization of short-reach optical channels. For a coherent optical transmission experiment …