Deep Quantile Regression for QoT Inference and Confident Decision Making

Abstract

This work examines deep quantile regression for quality-of-transmission (QoT) estimation and accurate decision making in optical networks. Quantile regression is applied to approximate QoT models capable of inferring QoT bounds for any future lightpath, according to a predefined level of certainty, for confident decision making, without the need to consider traditional margins at decision time. It is shown, that quantile regression automatically accounts for such margins, in a discriminative fashion, leading to a significant margin reduction and subsequently to more accurate inference of the QoT of unestablished lightpaths, when compared to the traditional margin-based decision approaches. Specifically, deep quantile regression for QoT estimation ensures that lightpaths with insufficient QoT will be accurately identified and rejected, while also identifying correctly lightpaths with sufficient QoT, making it a confident decision making tool for the planning of optical networks.