Poster Session Abstracts
Extended Kalman filter in optical fibre communications for joint compensation of phase and amplitude noise Cenqin Jin, Wenxiu Hu, Mark S. Leeson,
Machine learning enabled compensation of phase-to- amplitude distortion due to imperfect pump-dithering in optical phase conjugated transmission systems
Long H. Nguyen, Sonia Boscolo, Andrew D. Ellis , Stylianos Sygletos, Aston Institute of Photonic Technologies, Aston University We propose a machine learning-based digital signal processing technique to mitigate the impact of imperfect counter-phasing pump dithering in optical phase conjugated transmission systems.
Yunfei Chen, Tianhua Xu, School of Engineering, University of Warwick;
Mingming Tan, Aston Institute of Photonic Technologies, Aston University; Nikita A. Shevchenko, University College London; Qiankun Li, School of Physics, University of Electronic Science and Technology of China Morden long-haul optical fibre communication systems can be heavily degraded by linear and nonlinear transmission impairments, e.g. chromatic dispersion, polarization mode dispersion, laser phase noise, equalization enhanced phase noise, amplified spontaneous emission noise, fibre nonlinear interference. In this work, extended Kalman filter has been investigated to improve transmitted signal quality in long-haul Nyquist-spaced optical fibre communication systems. Strong capability of the extended Kalman filter on joint compensation of the phase and the amplitude noise has been observed and demonstrated. The performance of linear Kalman filter, Viterbi-Viterbi estimator and pilot- aid carrier phase estimator is also studied as benchmarks. Electronic dispersion compensation and digital nonlinearity compensation algorithms have also been applied in considered systems with reasonable laser phase noise from transmitter and local oscillator sources taken into account, to investigate the performance of extended Kalman filter in practical transmission scenarios.
Contrary to state-of-the-art approaches that can deal only with the residual phase distortion, our scheme also tackles the corresponding phase to amplitude transformations that have occurred in the dispersive channel. With the use of an adaptive configuration, we first track and compensate the dither induced phase deviations on the received signal and subsequently extrapolate and remove their amplitude impact. Through extensive numerical we explore the operational margins of our approach in terms of system transmission distance, constellation order and pump-phase mismatch level, and demonstrate significant performance improvement against current schemes.
Optical Amplifier Optimisation in Ultra-Wideband (UWB) Systems using Reinforcement Learning Shabnam Noor, Research Associate, Aston University
One of the key challenges in UWB systems is the development of optical amplifiers that can provide acceptable gain over wide bandwidths in a controlled and rapid manner. In the case of All-Raman amplification, multiple pumps, and thus, several input parameters need to be controlled.
Optimal Detector Collocation in QKD Networks using Twin-Field QKD Vasileios Karavias, PhD Student, University of Cambridge; Andrew Lord, BT;
Mike Payne, University of Cambridge We developed a mixed integer linear program to optimise the detector placement cost for collocated detectors using TF-QKD and showed that the model can deal with large graphs of circa
Moreover, in hybrid amplification, different types of amplifiers have different requirements. This becomes an even bigger challenge in dynamic optical networks under, e.g., various channel loading conditions. To this end, this work investigates a Reinforcement Learning framework, which uses real-time training data and interactive feedback, to achieve the optimum gain and NF by learning the best possible combination of input parameters. Unlike existing approaches, this does not require large amounts of training data and is not limited to a particular type of network scenario or amplifier. The approach will be verified in VPI-Python co- simulations and on All-Raman, as well as hybrid EDFA-Raman amplifier experimental testbeds.
100 nodes in under a minute. We considered networks where switches allow detectors to be shared between users. We used this model to investigate the effects of increases in switch loss and switching calibration time and showed that the cost of building a quantum network increases rapidly with these parameters. We showed that you can reduce the cost of the network by more than a factor of 3 by reducing the detector dark count rate at the expense of efficiency. We investigated the benefit of using cooled SNSPD detectors compared to SPADs and showed that on larger graphs the use of SNSPDs is favoured, even when the cooling cost of the node is 10 times higher than using a SPAD.
UKQNtel and Modelling multiplexed QKD networks Joseph Pearse, PhD Student, University of York
Optical Technology: The Unsung Hero Meeting Next Generation Infrastructure Requirements
I will describe the UKQNTel network, a QKD network between Cambridge University and Adastral Park with 3 trusted nodes and co- propagated classical and quantum data.
Raza Khan, Semtech Corporation, Senior market manager for Semtech’s Signal Integrity Products Group, Semtech Corporation All infrastructure must start with a strong foundation, including our telecommunication networks. The pandemic ushered in new
I will then describe the development of a model for QKD systems where classical and quantum data is co-propagated. By inputting parameters of the QKD links such as fibre lengths, powers, detector statistics, and QKD protocols, as well as details of the network structure, the model generates the QKD performance for each link in the network; including QBER, Visibility, and maximum Secret Key Rate. It can then determine the maximum Secret Key Rate between any two nodes in the network. The model includes greater detail than previous simulations, especially in regard to Raman scattering, visibility, and detection. This greater detail allows the model to be applicable over a greater range of quantum and channel wavelengths, allowing channel wavelengths between 1200 nm and 1900 nm. To corroborate the model, I will present data from real QKD systems, including UKQNtel.
connectivity requirements, as remote work became a new normal for many. Furthermore, the convergence of new IoT applications and 5G use cases—such as AI, cloud computing, autonomous driving, precision farming and more—continue to demand a fast and reliable network capable of supporting vast amounts of data. The success of 5G will be dependent upon its framework, which starts with optical technology. Optical technology is often overlooked as the key to making fast and reliable 5G a reality, yet, it will play a crucial role in delivering the high-bandwidth and low-latency requirements needed to support 5G, 5.5G, 6G and beyond. Additionally, the high- bandwidth and low-latency characteristics of optical technology will facilitate future key applications in smart cities, including smart grids for enhanced energy efficiency, sensory networks to improve public services, smart mobility for optimizing traffic and available city-wide network connections..
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