arXiv subjects
Publications and source records attributed to Morteza Kamalian-Kopae.
We demonstrate OESCL-band same-wavelength bi-directional transmission over 60 km HCF with 42.5 THz bandwidth, achieving GMIs comparable with the highest unidirectional SMF data-rates in both directions, with an aggregate of 423.7 + 426.5 Tb/s.
In this work we present a novel implementation of delay line free reservoir computing based on state-of-the-art photonic technologies, which exploits chaotic optical frequency comb formation in optical microresonator as the nonlinear reservoir. Our solution leverages the high resonator Q-factor both for memory and for enhancing high dimensional nonlinear mapping of input symbols. We numerically demonstrate the accurate prediction of about one thousand symbols in chaotic time series without the need of dedicated optimisation for specific tasks. Our results will enable design of optical neuromorphic computing architectures combining on-chip integrability, low footprint, high speed and low power consumption.
Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the challenge of noise accumulation. We propose here a new type of neural networks using stochastic resonances as an inherent part of the architecture and demonstrate a possibility of significant reduction of the required number of neurons for a given performance accuracy. We also show that such a neural network is more robust against the impact of noise.
The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is still a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large dispersion-induced memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while we have to keep an acceptable performance level of the simplified NN model. In this work, we address the complexity reduction problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), to mitigate the impairments for 30GBd 1000km transmission over a standard single-mode fiber, and demonstrate that it is feasible to reduce the equalizer's memory by up to 87.12%, and its complexity by up to 78.34%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense. Further, we examine the impact of using different CPU and GPU settings on the power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, by implementing the reduced NN equalizer on two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano, which are used to process the data generated via simulating the signal's propagation down the optical-fiber system.
State-of-the-art fiber Bragg grating interrogators utilize mature concepts and technologies like tunable lasers, optical spectrum analyzers and a combination of time, wavelength, or spatial division demultiplexing approaches. Here, we propose the use of computational compressed sensing (CS) techniques for interrogation of fiber Bragg gratings, reducing interrogator complexity by using a broadband ASE light source and single-pixel detection. We demonstrate temperature sensing using a pre-calibration approach to achieve reconstruction accuracy comparable to uncompressed measurements. We extend these principles for the interrogation of sensor networks, presenting strategies for tackling sparsity considerations. Our proof-of-principle demonstrations show how the presented computational compressed sensing techniques can provide an alternative for realizing low-complexity, small footprint interrogator configurations.