Enabling Technologies for Cognitive Optical Networks

Cognition is a new paradigm for optical networking, in which the network has capabilities to observe, plan, decide, and act autonomously in order to optimize the end-to-end performance and minimize the need for human supervision. This PhD thesis expands the state of the art on cognitive optical networks (CONs) and technologies enabling and supporting their implementation. The scientific content presented in this thesis tackles two major research problems. First, formulation of fundamental requirements and objectives of CONs, experimental evaluation of selected aspects of their architecture, and machine learning algorithms that make cognition possible. Secondly, advanced optical performance monitoring (OPM) capabilities performed via digital signal processing (DSP) that provide CONs with necessary feedback information allowing for autonomous network optimization. The research results presented in this thesis were carried out in the framework of the EU project Cognitive Heterogeneous Reconfigurable Optical Network (CHRON), whose aim was to develop an architecture and implement a testbed of a cognitive network able to self-configure and self-optimize to efficiently use available resources. In order to realize this objective, new CONsupporting functionalities had to be defined, developed, and experimentally verified. This thesis summarizes the main contributions of the author to the project. Cutting-edge results in experimental evaluation of functionalities of autonomous networks are presented: the first experimental demonstration of the use of case-based reasoning technique in optical communication network for successful quality of transmission estimation in an optical link; cognitively controlled erbium-doped fiber amplifiers to ensure below forward error correction limit performance of all transmitted channels; reconfigurable coherent software-defined receiver supporting various modulation formats and bit rates; experimental evaluation of required optical signal-to-noise ratio for flexible elastic optical networks with mixed modulation formats; evaluation of various prefilter shapes for high symbol rate software-defined transmitters. Furthermore, by using the DSP capabilities of a coherent software-defined receiver combined with powerful machine learning methods, optical performance monitoring techniques for next generation networks were conceived, implemented, and tested by numerical simulations and experiments. One of the highlights of this thesis is the first demonstration of a novel modulation format recognition method based on Stokes space parameters, capable of discerning between six different complex modulation formats. Moreover, new chromatic dispersion monitoring metrics were introduced and experimentally tested, while machine learning is shown to tackle constellation distortions due to nonlinearities in long haul fiber-optic links. In conclusion, the results presented in this thesis lay the groundwork for cognitive optical networks, as well as define and contribute to technologies required for their implementation. Operation of CON-enabling machine learning methods is tested experimentally and DSP-based OPM techniques for software-defined receivers are introduced and verified. The presented set of technologies forms a foundation, upon which next generation fiber-optic data transmission networks will be built.