Autonomous Constellation Shaping for Coherent Optical Fiber Communications

相干光纤通信的自主星座整形

• 批准号: RGPIN-2018-05491
• 负责人: Cartledge, John
• 金额: $ 6.7万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752055
• 关键词: Autonomous; Constellation; Shaping; Coherent; Optical

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.

光纤通信系统在整个Internet上广泛使用，使用户可以无缝发送和接收从数十公里到数千公里的距离进行的信息。对整个业务，教育，医疗保健和娱乐部门的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程序的需求的爆炸性增加正在加剧连通性的需求，只有通过光纤传输系统中的持续创新才能满足连接性。高速数字到分析转换器，模数转换器和数字信号处理的最新进展已导致光纤传输的新范式；基于现场调制，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可观的好处。拟议的研究解决了光网络面临的最紧迫问题：增加对单个光纤纤维传递的总比特率的需求，即传输能力。这项研究将重点放在星座形状上，因为（i）抑制通过在光纤内传播信号而产生的信号产生的性能限制非线性干扰（NLI）的产生，并且（ii）通过在设置可实现的光谱仪上提高NLI的传输能力来提高NLI的传输能力，以确保获得的光谱仪式（可实现的频率）。该塑料旨在最大化给定系统配置的可实现的，每通道光谱效率，从而最大程度地提高传输能力。该研究将在具有动态的，异质的渠道计划的弹性光网络（EON）的背景下进行，以支持多个数据速率，调制速率和频道频道。与具有静态，均匀频道计划的传统光学网络相比，这大大增加了设计最合适的星座塑造的挑战。在EON中，相邻的通道以及NLI在沿着路径的跨度随跨越感兴趣的通道所穿越的路径上可能会有所不同，并且随着网络适应的发生，跨度的相邻通道可能会随着时间而变化。因此，将研究基于机器学习技术（例如人工神经网络）的光学性能监测，作为一种从接收的信号中学习NLI的OSNR和属性的一种手段，从而可以自主优化星座的形状。该研究的主要结果是对用于自主优化星座塑造的方法的实验验证，由于设计形状的效力而引起的传输能力增加以及高素质的人员在前沿技术方面的熟练技术。