Objectives
Demonstration of AI-assisted network control strategies to proactively identify any soft failure of inline network equipment (amplifiers) and its utilization to reduce network downtime under the C+L Band optical network.
Deliverables
ML-assisted QoT Estimator: Estimates the quality of transmission (QoT) of lightpath with an accuracy of 1 dB margin while considering several input features (such as link length, launch power, etc.) of a light path as input.
ML-assisted QoT Degradation Classifier: Classify the combination of degrading in-line equipment while monitoring the QoT degradation of the lightpath with an accuracy >= 95%.
Network Simulator: Embedding the Classifier and QoT estimator into a state-of-art Multiband (C+L band) network simulator and demonstrating network automation by taking pre-emptive actions to prevent network downtime.
Product Phase of Deliverables: As provisioned by DOT to demonstrate the performance of the classifier over Tejas/C-DOT's testbed.
Co-Principal Investigator:

Anand Srivastava
Professor
Indraprastha Institute of Information Technology (IIIT) Delhi
Principal Investigator:

Abhijit Mitra
Assistant Professor
Indraprastha Institute of Information Technology (IIIT) Delhi
WP14
AI-Enabled Pre-Emptive Network Control in Multiband Optical Networks
Organization:



No. of Members:
2
Member Details:
-