Dispersion-managed fiber echo state network analogue with high (including THz) bandwidth

We propose a design for high (including THz) bandwidth neuromorphic signal processing based on fiber echo state network analogue and demonstrate through numerical modeling its efficiency for distortion mitigation in optical communications with high-order (including 1024-QAM) formats. This is achieved via all-optical implementation of masking and neural functionality by utilizing dispersion and nonlinear properties of the fiber. The design is flexible and format-transparent making it relevant for future communication systems. The model is simple to implement, as it requires only two attenuators and pumps in addition to a traditional nonlinear optical loop mirror (NOLM), and offers a vast range of optimization parameters

Multidimensional fiber echo state network analogue

Abstract: Optical neuoromorphic technologies enable neural network-based signal processing through a specifically designed hardware and may confer advantages in speed and energy. However, the advances of such technologies in bandwidth and/or dimensionality are often limited by the constraints of the underlying material. Optical fiber presents a well-studied low-cost solution with unique advantages for low-loss high-speed signal processing. The fiber echo state network analogue (FESNA), fiber-based neuromorphic processor, has been the first technology suitable for multichannel high bandwidth (including THz) and dual-quadrature signal processing. Here we propose the multidimensional FESNA (MD-FESNA) processing by utilizing multi-mode fiber non-linearity. Thus, the developed MD-FESNA is the first neuromorphic technology which augments all aforementioned advantages of FESNA with multidimensional spatio-temporal processing. We demonstrate the performance and flexibility of the technology on the example of prediction tasks for hyperchaotic systems. These results will pave the way for a high-speed neuromorphic processing of multidimensional tasks, hardware for spatio-temporal neural networks and open new application venues for fiber-based spatio-temporal multiplexing

Shannon capacity of nonlinear communication channels

The exponentially increasing demand on operational data rate has been met with technological advances in telecommunication systems such as advanced multilevel and multidimensional modulation formats, fast signal processing, and research into new different media for signal transmission. Since the current communication channels are essentially nonlinear, estimation of the Shannon capacity for modern nonlinear communication channels is required. This PhD research project has targeted the study of the capacity limits of different nonlinear communication channels with a view to enable a significant enhancement in the data rate of the currently deployed fiber networks. In the current study, a theoretical framework for calculating the Shannon capacity of nonlinear regenerative channels has been developed and illustrated on the example of the proposed here regenerative Fourier transform (RFT). Moreover, the maximum gain in Shannon capacity due to regeneration (that is, the Shannon capacity of a system with ideal regenerators – the upper bound on capacity for all regenerative schemes) is calculated analytically. Thus, we derived a regenerative limit to which the capacity of any regenerative system can be compared, as analogue of the seminal linear Shannon limit. A general optimization scheme (regenerative mapping) has been introduced and demonstrated on systems with different regenerative elements: phase sensitive amplifiers and the proposed here multilevel regenerative schemes: the regenerative Fourier transform and the coupled nonlinear loop mirror

Multi-channel optical neuromorphic processor for frequency-multiplexed signals

Here we develop the first optical neuromorphic processor for frequency-multiplexed and multi-channel signals and demonstrate its application for orthogonal frequency-division multiplexing and wavelength-division multiplexing. The presented architecture supports multichannel operation through incorporation of the optical Fourier transform and dispersion-managed fiber echo state network analogue enabling processing of dual-quadrature high bandwidth signals. We demonstrate applicability of the design for prediction and equalization tasks. The proposed technology paves the way to multi-channel neuromorphic signal processing

Sparse Identification for Nonlinear Optical communication systems

We have developed a low complexity machine learning based nonlinear impairment equalization scheme and demonstrated its successful performance in SDM transmission links achieving compensation of both inter- and intra- channel Kerr-based nonlinear effects. The method operates in one sample per symbol and in one computational step. It is adaptive, i.e. it does not require a knowledge of system parameters, and it is scalable to different power levels and modulation formats. The method can be straightforwardly expanded to multi-channel systems and to any other type of nonlinear impairment

Information theory analysis of regenerative channels

In this paper we summarize our recently proposed work on the information theory analysis of regenerative channels. We discuss how the design and the transfer function properties of the regenerator affect the noise statistics and enable Shannon capacities higher than that of the corresponding linear channels (in the absence of regeneration)

Fiber echo state network analogue for high-bandwidth dual-quadrature signal processing

All-optical platforms for recurrent neural networks can offer higher computational speed and energy efficiency. To produce a major advance in comparison with currently available digital signal processing methods, the new system would need to have high bandwidth and operate both signal quadratures (power and phase). Here we propose a fiber echo state network analogue (FESNA) — the first optical technology that provides both high (beyond previous limits) bandwidth and dual-quadrature signal processing. We demonstrate applicability of the designed system for prediction tasks and for the mitigation of distortions in optical communication systems with multilevel dual-quadrature encoded signals

Perturbative discrete-time multivariate fiber channel model with finite memory

We introduce a discrete-time fibre channel model that provides an accurate analytical description of signal-signal and signal-noise interference with memory defined by the interplay of nonlinearity and dispersion. Also the conditional pdf of signal distortion, which captures non-circular complex multivariate symbol interactions, is derived providing the necessary platform for the analysis of channel statistics and capacity estimations in fibre optic links

Fiber-Optic Reservoir Computing for QAM-Signal Processing

Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicability for QAM-signal processing. The FORC enables over 5 dB improvement due to mitigating nonlinear distortions and supports high complexity QAM-formats

Design of multilevel amplitude regenerative system

We propose a scheme for multilevel (nine or more) amplitude regeneration based on a nonlinear optical loop mirror (NOLM) and demonstrate through numerical modeling its efficiency and cascadability on circular 16-, 64-, and 256- symbol constellations. We show that the amplitude noise is efficiently suppressed. The design is flexible and enables variation of the number of levels and their positioning. The scheme is compatible with phase regenerators. Also, compared to the traditional single-NOLM configuration scheme, new features, such as reduced and sign-varied power-dependent phase shift, are available. The model is simple to implement, as it requires only two couplers in addition to the traditional NOLM, and offers a vast range of optimization parameters