A Joint OSNR and Nonlinear Distortions Estimation Method for Optical Fiber Transmission System

Abstract

In this paper, we propose and demonstrate a joint optical signal-to-noise ratio (OSNR) and a nonlinear distortions estimation method. The OSNR is calculated by considering either the link parameters or the correlation operations of the training symbols. The nonlinear distortions estimation in this calculation comes from different nonlinear sensitivities of these two methods. The indicator of NL penalty is defined as the ratio of nonlinear distortion power to the signal power. As the estimation method, based on link parameters, has low computational complexity, and the training symbol used for the OSNR estimation is inherently indispensable for frame synchronization, this nonlinear distortion estimation method introduces a few complexities and no extra overhead. Experimental results obtained by using a 200 Gb/s 16 QAM DWDM system establish the accuracy of the proposed OSNR estimations method, with errors less than 1 dB. The nonlinear distortions were also measured under different simulation conditions, using a coherent optical OFDM system, and the results are in accordance with the expectations.

Fighting Fire with Light: A Case for Defending DDoS Attacks Using the Optical Layer

“…If these backup wavelengths are added in the same order of time as a TBE operation, it is possible to mitigate the attack in ∼5 seconds. We think this is entirely possible, given the innovations in the optical community including programmable transceivers [2], optical amplifiers for reconfigurable networks [22,46], throughput gains of elastic transceivers in long-haul networks [23], and recent momentum in the networking community [38,45,14]. Thus, we implore researchers in the security community to consider optical layer when designing and modeling the next generation of solutions for DDoS attacks.…”

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Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers

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