Showing papers by "Danish Rafique published in 2019"

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Abstract: Operators' network management continuously measures network health by collecting data from the deployed network devices; data is used mainly for performance reporting and diagnosing network problems after failures, as well as by human capacity planners to predict future traffic growth. Typically, these network management tools are generally reactive and require significant human effort and skills to operate effectively. As optical networks evolve to fulfil highly flexible connectivity and dynamicity requirements, and supporting ultra-low latency services, they must also provide reliable connectivity and increased network resource efficiency. Therefore, reactive human-based network measurement and management will be a limiting factor in the size and scale of these new networks. Future optical networks must support fully automated management, providing dynamic resource re-optimization to rapidly adapt network resources based on predicted conditions and events; identify service degradation conditions that will eventually impact connectivity and highlight critical devices and links for further inspection; and augment rapid protection schemes if a failure is predicted or detected, and facilitate resource optimization after restoration events. Applying automation techniques to network management requires both the collection of data from a variety of sources at various time frequencies, but it must also support the capability to extract knowledge and derive insight for performance monitoring, troubleshooting, and maintain network service continuity. Innovative analytics algorithms must be developed to derive meaningful input to the entities that orchestrate and control network resources; these control elements must also be capable of proactively programming the underlying optical infrastructure. In this article, we review the emerging requirements for optical network management automation, the capabilities of current optical systems, and the development and standardization status of data models and protocols to facilitate automated network monitoring. Finally, we propose an architecture to provide Monitoring and Data Analytics (MDA) capabilities, we present illustrative control loops for advanced network monitoring use cases, and the findings that validate the usefulness of MDA to provide automated optical network management.

Fault Management Based on Machine Learning

Abstract: Machine Learning (ML) brings many benefits for network operation. In this paper, basic ML concepts and its integration into existing network control and management planes are reviewed. Case studies covering fault management are illustrated.

Machine Learning Based Laser Failure Mode Detection

Abstract: Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (LSTM) recurrent neural networks to detect the different laser degradation modes based on synthetic historical failure data. In comparison to typical threshold-based systems, attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms classical machine learning (ML) models namely Random Forest (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR).

64-GBd DP-Bipolar-8ASK Transmission Over 120 km SSMF Employing a Monolithically Integrated Driver and MZM in 0.25-μm SiGe BiCMOS Technology

Abstract: We demonstrate 64-GBd signal generation up to bipolar-8-ASK utilizing a single MZM, monolithically integrated with segmented drivers in SiGe. Using polarization multiplexing, 300-Gb/s net data rate transmission over 120 km SSMF is shown.