Transfer learning Aided QoT Computation in Network Operating with the 400ZR Standard

The current increase in bandwidth-hungry applications and the progressively evolving concept of connected "smart" devices through the internet have increased internet traffic exponentially. To hold this expansion of internet traffic, the network operators insist on the full capacity utilization of already deployed hardware infrastructure. In this context, accurate and earlier calculation of the quality of transmission (QoT) of the lightpaths (LPs) is critical for minimizing the required margins that arise due to the uncertainty in the operating point of network elements. This article proposes a novel framework in which a transfer learning assisted QoT-Estimation (QoT-E) is made. The transfer learning agent acquired the knowledge from a traditional fully operational network operating on C-band and utilized this knowledge to assist the operator in estimating the LP QoT on a state-of-the-art newly functioning network on an extended C-band operating with 400ZR standards. The measurement parameter considered to estimate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The dataset used in this analysis is generated synthetically by utilizing well tested GNPy platform. Promising results are achieved in terms of reducing the overall required margin and better utilization of the residual network capacity.