ADVA Optical Networking SE

Security of data transported in telecommunication networks is of primary importance for internet service providers as data safety is at the basis of the mutual trust with customers. Quantum key distribution (QKD) recently gained a renewed interest. However, some constraints of optical transport networks make sometimes complicated the deployment of such QKD systems in the field. In this paper, we report the transmission of a coherent 400-Gbps dual-polarization 16QAM channel that transports QKD-secured 100-GbEthernet data stream with other fifty-four WDM channels at 100-Gbps over 184-km of standard single mode fiber (SSMF) through three QKD links (of 66-km, 50-km and 66-km, respectively) and two trusted nodes. On the two longest sections of the link, the quantum key and WDM signals are propagated on two different fibers, while on the shortest section of 50 km the QKD signal and WDM comb co-propagate on a same fiber. Secret key rates (SKR) and quantum bit error rate (QBER) are measured and reported on each of the three fiber sections. Various analyses are carried out on the co-propagation section of 50-km: the SKR/QBER sensitivity to the optical power of the WDM comb, or to the optical signal-to-noise ratio (OSNR) of the WDM channels co-propagating with the quantum signal is studied. The unique arrangement of the most advanced QKD and WDM technologies reported here constitutes, in our opinion, a world first, and demonstrates that QKD and WDM solutions can coexist in already deployed optical transport networks.

Optical spectrum as a service (OSaaS) spanning over multiple transparent optical network domains can significantly reduce the investment and operational costs of the end-to-end service. Based on the black-link approach, these services are empowered by reconfigurable transceivers and the emerging disaggregation trend in optical transport networks. This work investigates the accuracy aspects of the channel probing method used in generalized signal-to-noise ratio (GSNR)-based OSaaS characterization in terrestrial brownfield systems. OSaaS service margins to accommodate impacts from enabling neighboring channels and end-of-life channel loads are experimentally derived in a systematic lab study carried out in the Open Ireland testbed. The applicability of the lab-derived margins is then verified in the HEAnet production network using a 400 GHz wide OSaaS. Finally, the probing accuracy is tested by depleting the GSNR margin through power adjustments utilizing the same 400 GHz OSaaS in the HEAnet live network. A minimum of 0.92 dB and 1.46 dB of service margin allocation is recommended to accommodate the impacts of enabling neighboring channels and end-of-life channel loads. A further 0.6 dB of GSNR margin should be allocated to compensate for probing inaccuracies.

Codirectional Raman amplification with higher–order pumping is a promising solution for improving performance of commercial unrepeatered submarine links and thus to allow for an upgrade to advanced modulation formats offering larger capacity. However, power fluctuations of the involved high–power pump induce phase shifts in phase modulated signals via the nonlinear Kerr effect that destroy the theoretically possible performance improvement. A technique for reducing the impact of this effect on system performance by jointly processing signals of neighboring channels and evaluating the phase of both channels at a time for symbol detection is presented and evaluated.

Passive optical networks (PONs) have become a promising broadband access network solution thanks to their wide bandwidth, low-cost deployment and maintenance, and scalability. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize network faults and thus reduce maintenance costs, minimize downtime, and enhance quality of service. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal, the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. OTDR is a technique commonly used for monitoring of fiber optic links. However, faulty branch isolation becomes very challenging when the reflections originate from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML)-based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade if the PON system (adopted for the generation of the training data) has changed, e.g., by adding more branches or varying the length difference between two neighboring branches, etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from the PON system.

To analyse and evaluate the physical layer performance of a UK national quantum network (UKQNtel) utilising COW-QKD, in O band, integrated with 500 Gb/s encrypted data, in C band, over 121 km between BT Labs and the University of Cambridge, a theoretical model encompassing COW-QKD and Raman scattering noise is developed and fitted with real-world experimental data. Different physical mechanisms underpinning the link performance are identified and discussed. The model developed in this work also provides a preliminary technical guidance for future UKQNtel upgrade and expansion, as well as potentially useful insights for quantum-secured networks design prior to practical deployment.

Lasers are widely regarded as the most critical components in optical communication systems. Their reliability has a significant impact on the system's availability and performance. Conventionally, the methods adopted for laser reliability estimation are based on extrapolation to experimental reliability data derived from accelerated aging tests, and could lead to either overestimation or underestimation of actual laser lifetime. Alternatively, machine learning-based approaches have shown great promise in improving laser reliability and outperforming traditional methods. Nonetheless, one major barrier to the adoption of ML methods is their lack of interpretability as they operate as black-box methods. To address this issue, we propose an interpretable ML model for laser lifetime prediction. A Shapley additive explanations (SHAP) method is used to explain the predictions made by the ML approach by quantifying the relevance of inputs, clarifying their effects on each individual estimation, and emphasizing their interaction. The proposed approach is validated using synthetic reliability data. The results show that the ML model achieves a good predictive performance (yielding a root mean square error of 0.37 years). The interpretability analysis shows that the slope efficiency and the power are the top two most significant features impacting the laser lifetime predictions made by the ML model. The predictive accuracy of the ML model can be further improved slightly after re-examination of features.