Multi-Link Failure Effects on MPLS Resilient Fast-Reroute Network Architectures

© 2021 IEEE.MPLS has been in the forefront of high-speed Wide Area Networks (WANs), for almost two decades [1, 12]. The performance advantages in implementing Multi-Protocol Label Switching (MPLS) are mainly its superior speed based on fast label switching and its capability to perform Fast Reroute rapidly when failure(s) occur – in theory under 50 ms [16, 17], which makes MPLS also interesting for real-time applications. We investigate the aforementioned advantages of MPLS by creating two real testbeds using actual routers that commercial Internet Service Providers (ISPs) use, one with a ring and one with a partial mesh architecture. In those two testbeds we compare the performance of MPLS channels versus normal routing, both using the Open Shortest Path First (OSPF) routing protocol. The speed of the Fast Reroute mechanism for MPLS when failures are occurring is investigated. Firstly, baseline experiments are performed consisting of MPLS versus normal routing. Results are evaluated and compared using both single and dual failure scenarios within the two architectures. Our results confirm recovery times within 50 ms

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Reduction of Nonlinear Intersubcarrier Intermixing in Coherent Optical OFDM by a Fast Newton-Based Support Vector Machine Nonlinear Equalizer

A fast Newton-based support vector machine (N-SVM) nonlinear equalizer (NLE) is experimentally demonstrated, for the first time, in 40 Gb/s 16-quadrature amplitude modulated coherent optical orthogonal frequency division multiplexing at 2000 km of transmission. It is shown that N-SVM-NLE extends the optimum launched optical power by 2 dB compared to the benchmark Volterra-based NLE. The performance improvement by N-SVM is due to its ability of tackling both deterministic fiber-induced nonlinear effects and the interaction between nonlinearities and stochastic noises (e.g., polarization-mode dispersion). An N-SVM is more tolerant to intersubcarrier nonlinear crosstalk effects than Volterra-based NLE, especially when applied across all subcarriers simultaneously. In contrast to the conventional SVM, the proposed algorithm is of reduced classifier complexity offering lower computational load and execution time. For a low C-parameter of 4 (a penalty parameter related to complexity), an execution time of 1.6 s is required for N-SVM to effectively mitigate nonlinearities. Compared to conventional SVM, the computational load of N-SVM is ∼6 times lower

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.Peer reviewe

Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM

This document is the Accepted Manuscript of the following article: E. Giacoumidis, et al, 'Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM', Vol. 30 (12): 1091-1094, June 2018. Under embargo until 4 May 2020. The final, published version is available online at doi: https://doi.org/10.1109/LPT.2018.2832617 © 2018 IEEEA novel blind nonlinear equalization (BNLE) technique based on the iterative re-weighted least square is experimentally demonstrated for single- and multi-channel coherent optical orthogonal frequency-division multiplexing. The adopted BNLE combines, for the first time, a support vector machine-learning cost function with the classical Sato or Godard error functions and maximum likelihood recursive least-squares. At optimum launched optical power, BNLE reduces the fiber nonlinearity penalty by ~1 (16-QAM single-channel at 2000 km) and ~1.7 dB (QPSK multi-channel at 3200 km) compared to a Volterra-based NLE. The proposed BNLE is more effective for multi-channel configuration: 1) it outperforms the “gold-standard” digital-back propagation and 2) for a high number of subcarriers the performance is better due to its capability of tackling inter-subcarrier four-wave mixing.Peer reviewe

Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM

Mutsam A. Jarajreh, et al, 'Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM', IEEE Photonics Technology Letters, Vol. 27 (4), February 2015, available online at: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6975096&tag=1.We propose a novel low-complexity artificial neural network (ANN)-based nonlinear equalizer (NLE) for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) and compare it with the recent inverse Volterra-series transfer function (IVSTF)-based NLE over up to 1000 km of uncompensated links. Demonstration of ANN-NLE at 80-Gb/s CO-OFDM using 16-quadrature amplitude modulation reveals a Q-factor improvement after 1000-km transmission of 3 and 1 dB with respect to the linear equalization and IVSTF-NLE, respectively.Peer reviewe

Effective handling of nonlinear distortions in CO-OFDM using affinity propagation clustering

© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.We experimentally demonstrate a system-agnostic and training-data-free nonlinearity compensator, using affinity propagation (AP) clustering in single- and multi-channel coherent optical OFDM (CO-OFDM) for up to 3200 km transmission. We show that AP outperforms benchmark deterministic and clustering algorithms by effectively tackling stochastic nonlinear distortions and inter-channel nonlinearities. AP offers up to almost 4 dB power margin extension over linear equalization in single-channel 16-quadrature amplitude-modulated CO-OFDM and a 1.4 dB increase in Q-factor over digital back-propagation in multi-channel quaternary phase-shift keying CO-OFDM. Simulated results indicate transparency to higher modulation format orders and better efficiency when a multi-carrier structure is considered.Peer reviewe

Volterra-Based Reconfigurable Nonlinear Equalizer for Coherent OFDM

Elias Giacoumidis, et al, 'Volterra-Based Reconfigurable Nonlinear Equalizer for Coherent OFDM', IEEE Photonics Technology Letters, Vol 26 (14): 1383-1386, June 2014, doi: https://doi.org/10.1109/LPT.2014.2321434. Published by IEEE.A reconfigurable nonlinear equalizer (RNLE) based on inverse Volterra series transfer function is proposed for dual-polarization (DP) and multiband coherent optical orthogonal frequency-division multiplexing (OFDM) signals. It is shown that the RNLE outperforms by 2 dB the linear equalization in a 260-Gb/s DP-OFDM system at 1500 km. The RNLE improves the tolerance to inter/intraband nonlinearities, being independent on polarization tributaries, modulation format, signal bit rate, subcarrier number, and distance.Peer reviewe