Iterative Supervised Learning Approach Using Transceiver Bit-Error-rate Measurements for Optical Line System Optimization

Defining the working points of optical amplifiers is a key factor when managing optical networks, particularly for the quality of transmission (QoT) of deployed connections. However, given the lack of knowledge of physical layer parameters, in many cases operators use these infrastructures suboptimally. In this work, a methodology is presented that optimizes the QoT of an optical line system (OLS) by setting the working points of the erbium-doped fiber amplifiers (EDFAs), by analysis of simulations that use synthetic data derived from experimental characterization of commercial devices. The procedure is divided into three phases: a physical layer characterization, a design process, and an iterative supervised learning approach. Within the first phase, a novel amplifier physical layer characterization is used, exploiting a simple EDFA model that allows an efficient estimation of the OLS behavior, knowing only the setting operative ranges of the devices. The results show that the satisfactory outcome produced during the design phase is further improved by the iterative supervised learning approach. The latter approach is implemented for single OLSs between couples of adjacent reconfigurable optical add and drop multiplexers, each equipped with a certain set of transceivers, enabling the QoT estimation of the specific OLS.