Intelligent data aggregation using autoencoders and other statistics
Document typeMaster thesis
Rights accessOpen Access
Optical constellations offer a highly dimensional representation of the signals in optical transport network technologies and they can be analyzed for several use cases such as optical network health analysis and secure optical networks. It is crucial for operators to implement efficient monitoring architectures that mitigate potential drawbacks (such as high capacity exhaustion) while ensuring the validity and reliability of widely collected monitoring data. In this project we aim to provide robust techniques for optical constellation analysis and compression achieving large compression rates with negligible information loss. In particular, optical constellations will be characterised through parametric probability distributions and compressed through autoencoder architectures.