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Register a new userBandwidth-Efficient Synchronization for Fiber Optic Transmission: System Performance Measurements
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Added by
Oluyemi Omomukuyo
Publication date
2018
fields
Electronic Engineering
and research's language is
English
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In this article, we first provide a brief overview of optical transmission systems and some of their performance specifications. We then present a simple, robust, and bandwidth-efficient OFDM synchronization method, and carry out measurements to validate the presented synchronization method with the aid of an experimental setup.
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The Maunakea Spectroscopic Explorer (MSE) is a next-generation observatory, designed to provide highly multiplexed, multi-object spectroscopy over a wide field of view. The observatory will consist of (1) a telescope with an 11.25 m aperture, (2) a 1.5 square-degree science field of view, (3) fibre optic positioning and transmission systems, and (4) a suite of low (R=3000), moderate (R=6000) and high resolution (R=40,000) spectrographs. The Fibre Transmission System (FiTS) consists of 4332 optical fibres, designed to transmit the light from the telescope prime focus to the dedicated spectrographs. The ambitious science goals of MSE require the Fibre Transmission System to deliver performance well beyond the current state of the art for multi-fibre systems, e.g., the sensitivity to observe magnitude 24 objects over a very broad wavelength range (0.37 - 1.8 microns) while achieving relative spectrophotometric accuracy of <3% and radial velocity precision of 20 km/s.
We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrodinger equation (NLSE). Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities. We exploit this connection by parameterizing the SSM and viewing the linear steps as general linear functions, similar to the weight matrices in a neural network. The resulting physics-based machine-learning model has several advantages over black-box function approximators. For example, it allows us to examine and interpret the learned solutions in order to understand why they perform well. As an application, low-complexity nonlinear equalization is considered, where the task is to efficiently invert the NLSE. This is commonly referred to as digital backpropagation (DBP). Rather than employing neural networks, the proposed algorithm, dubbed learned DBP (LDBP), uses the physics-based model with trainable filters in each step and its complexity is reduced by progressively pruning filter taps during gradient descent. Our main finding is that the filters can be pruned to remarkably short lengths-as few as 3 taps/step-without sacrificing performance. As a result, the complexity can be reduced by orders of magnitude in comparison to prior work. By inspecting the filter responses, an additional theoretical justification for the learned parameter configurations is provided. Our work illustrates that combining data-driven optimization with existing domain knowledge can generate new insights into old communications problems.
The first stage of the construction of the deep underwater neutrino telescope Baikal-GVD is planned to be completed in 2024. The second stage of the detector deployment is planned to be carried out using a data acquisition system based on fibre optic technologies, which will allow for increased data throughput and more flexible trigger conditions. A dedicated test facility has been built and deployed at the Baikal-GVD site to test the new technological solutions. We present the principles of operation and results of tests of the new data acquisition system.
We use white Gaussian noise as a test signal for single-mode and multimode transmission links and estimate the link capacity based on a calculation of mutual information. We also extract the complex amplitude channel estimations and mode-dependent loss with high accuracy.
We build a resonant fiber optic gyro based on Kagome hollow-core fiber. A semi-bulk cavity architecture based on a 18-m-long Kagome fiber permits to achieve a cavity finesse of 23 with a resonance linewidth of 700 kHz. An optimized Pound-Drever-Hall servo-locking scheme is used to probe the cavity in reflection. Closed-loop operation of the gyroscope permits to reach an angular random walk as small as 0.004$^circ/sqrt{mathrm{h}}$ and a bias stability of 0.45$^circ$/h over 0.5 s of integration time.
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