Abstract:
With the increase in global traffic, the pressure on the operators to provide
services with optimal capacity is also increasing. The information about the
quality of transmission is required to make routing decisions such as deciding
whether to establish the lightpath or not. The traditional approaches used
to estimate the QoT of new lightpaths are coupled with high margins for
accurate estimation but this results in under-utilization of network elements.
Machine learning models can be used to predict the QoT accurately with low
margins resulting in better utilization of network elements thus increasing the
efficiency of network. Models are trained on the data obtained from previous
knowledge of already deployed network and is then used to predict the QoT
of lightpaths. We apply Machine Learning models on synthetic data of real
network topologies. We expect to predict the QoT (Generalized Signal to
Noise Ratio (GSNR)) of lightpaths with minimum error
