Autonomous Constellation Shaping for Coherent Optical Fiber Communications

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Optical fiber communication systems are used extensively throughout the Internet, allowing users to seamlessly send and receive information traversing distances from tens of kilometers to thousands of kilometers. The explosive increase in demand for cloud-based storage and services, social media services, video streaming, and machine-to-machine applications across the business, educational, healthcare and entertainment sectors is fueling a need for connectivity that can only be met by continued innovation in optical fiber transmission systems. Recent advances in high-speed digital-to-analog converters, analog-to-digital converters, and digital signal processing have led to a new paradigm in optical fiber transmission; systems based on field modulation, coherent detection, and digital signal processing provide substantial benefits in terms of performance and functionality.***The proposed research addresses the most pressing problem facing optical networks: the need to increase the aggregate bit rate transmitted over a single optical fiber, i.e., the transmission capacity. The research will focus on constellation shaping as it can (i) suppress the generation of performance-limiting nonlinear interference (NLI) generated by propagating signals due to intra- and inter-channel effects in an optical fiber and (ii) increase the transmission capacity in the presence of NLI by providing substantial flexibility in setting the achievable spectral efficiency for a given received optical signal-to-noise ratio (OSNR). The shaping is aimed at maximizing the achievable, per-channel spectral efficiency, and hence the transmission capacity, for a given system configuration.***The research will be pursued in the context of elastic optical networks (EONs) with dynamic, heterogeneous channel plans supporting multiple data rates, modulation formats, and channel bandwidths. This substantially increases the challenge of devising the most suitable constellation shaping compared to conventional optical networks with static, homogeneous channel plans. In EONs, the neighbouring channels, and hence the NLI, can vary from span to span along the path traversed by a channel of interest, and the neighbouring channels on a span can change with time as network adaptation occurs. Consequently, optical performance monitoring based on machine learning techniques, such as artificial neural networks, will be investigated as a means of learning the OSNR and properties of the NLI from the received signal, thereby allowing the constellation shaping to be autonomously optimized. ***The primary outcomes of the research are experimental validations of the methodologies developed for autonomously optimizing the constellation shaping, demonstrations of increased transmission capacity due to the efficacies of the devised shapings, and highly qualified personnel skilled in leading edge technologies.**