Field-Enhanced Filtering in MIMO Learned Volterra Nonlinear Equalisation of Multi-Wavelength Systems

We propose a novel MIMO-WDM Volterra-based nonlinear-equalisation scheme with adaptive time-domain nonlinear stages enhanced by filtering in both the power and optical signal waveforms. This approach efficiently captures the interplay between dispersion and non-linearity in each step, leading to $46\%$ complexity reduction for $9\times 9$-MIMO operation.

Complex-Valued Kernel-based Phase and Amplitude Distortion Compensation in Parametrically Amplified Optical Links

We develop a complex-valued kernel-adaptive-filtering based method for phase and amplitude distortion compensation in cascaded fibre-optical parametric amplifier (FOPA) links. Our algorithm predicts and cancels both distortions induced by pump-phase modulation across all amplification stages, achieving more than an order of magnitude improvement in BER.

Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.