Autonomous Constellation Shaping for Coherent Optical Fiber Communications

光纤通信系统在整个互联网中广泛使用，允许用户无缝地发送和接收距离从几十公里到几千公里的信息。商业、教育、医疗保健和娱乐领域对基于云的存储和服务、社交媒体服务、视频流以及机器对机器应用的需求爆炸式增长，推动了对连接的需求，而这种需求只能通过光纤传输系统的持续创新来满足。高速数模转换器、模数转换器和数字信号处理的最新进展导致了光纤传输的新范式；基于场调制、相干检测和数字信号处理的系统在性能和功能方面提供了实质性的好处。***提出的研究解决了光网络面临的最紧迫的问题：需要增加在单个光纤上传输的总比特率，即传输容量。该研究将侧重于星座塑造，因为它可以(i)抑制由于光纤通道内和通道间效应而产生的信号传播产生的性能限制非线性干扰（NLI）的产生；（ii）在NLI存在的情况下，通过为给定的接收光信噪比（OSNR）提供设置可实现的频谱效率的实质性灵活性，增加传输容量。整形的目的是最大化可实现的，每通道频谱效率，因此传输容量，为给定的系统配置。该研究将在弹性光网络（EONs）的背景下进行，该网络具有动态、异构信道计划，支持多种数据速率、调制格式和信道带宽。与具有静态、均匀信道计划的传统光网络相比，这大大增加了设计最合适的星座形状的挑战。在eon中，相邻通道以及NLI可以沿着感兴趣的通道所穿越的路径在不同的跨度上变化，并且随着网络适应的发生，一个跨度上的相邻通道可以随时间变化。因此，基于机器学习技术（如人工神经网络）的光学性能监测将被研究作为一种从接收信号中学习NLI的OSNR和特性的手段，从而允许星座成形自主优化。***该研究的主要成果是自主优化星座成形方法的实验验证，由于设计的成形效率而增加的传输容量的演示，以及熟练掌握前沿技术的高素质人员

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.**