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Abstract. Transparent optical networks need novel connection management approaches to take into account the presence of physical impairments in lightpath provisioning. Two main schemes are emerging from literature when considering how to introduce impairment-aware mechanisms in a distributed optical control plane like GMPLS. A well-known approach is based on extending the routing protocol to compute an optically-feasible light-path. Lately, a new approach is emerging which keeps the routing protocol unmodified while leveraging on signaling protocol extensions to find the proper lightpath for the incoming connection request. The aim of this paper is to prove that the signaling-based approach has several advantages compared to the routing-based one, in term of scalability and robustness especially when link information changes are frequent in the network. Simulation results show that a signaling-based approach is much more robust to inaccurate information about network status, therefore it is a suitable approach for considering physical impairments in dynamic optical networks.

In the direction of disaggregated and cognitive optical networks, this work proposes and experimentally tests a vendor-agnostic optical line controller architecture capable of autonomously setting the working point of optical amplifiers to maximize the capacity of a ROADM-to-ROADM (reconfigurable optical add–drop multiplexer) link. From a procedural point of view, once the equipment is installed, the presented software framework performs an automatic characterization of the line, span by span, to abstract the properties of the physical layer. This process requires the exploitation of monitoring devices such as optical channel monitors and optical time domain reflectometers, available, in a future perspective, in each amplification site. On the basis of this information, an optimization algorithm determines the working point of each amplifier to maximize the quality of transmission (QoT) over the entire band. The optical line controller has been experimentally tested in the laboratory using two different control strategies, achieving in both cases a homogeneous QoT for each channel close to the maximum average and an excellent match with respect to emulation results. In this framework, the Gaussian noise simulation in Python (GNPy) open source Python library is used as the physical model for optical propagation through the fiber, and the covariance matrix adaptation evolution strategy is used as an optimization algorithm to identify properties of each fiber span and to maximize the link capacity.

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