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.

光纤通信系统广泛应用于整个互联网，使用户能够无缝地发送和接收从几十公里到数千公里的距离的信息。商业、教育、医疗和娱乐行业对基于云的存储和服务、社交媒体服务、视频流和机器对机器应用的需求呈爆炸性增长，这推动了对连接的需求，而只有通过光纤传输系统的持续创新才能满足这一需求。在高速数模转换器、模数转换器和数字信号处理方面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巨大的好处。研究将集中于星座整形，因为它可以(I)抑制由于光纤中的信道内和信道间效应而由传播信号产生的性能限制的非线性干扰(NLI)的产生，以及(Ii)通过在为给定的接收光信噪比(OSNR)设置可实现的频谱效率方面提供相当大的灵活性来增加存在NLI的情况下的传输容量。对于给定的系统配置，整形旨在最大化可实现的每信道频谱效率，从而最大化传输容量。研究将在弹性光网络(EON)的背景下进行，该弹性光网络具有支持多种数据速率、调制格式和信道带宽的动态、异质的信道计划。与具有静态、同质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成形的挑战。在亿万年中，相邻的信道以及因此的NLI可以沿着感兴趣的信道所经过的路径随跨度而变化，并且随着网络自适应的发生，跨度上的相邻信道可以随时间而改变。因此，将研究基于诸如人工神经网络的机器学习技术的光学性能监测，作为从接收信号中学习NLI的OSNR和属性的手段，从而允许自动优化星座成形。研究的主要成果是对为自主优化星座整形而开发的方法进行了实验验证，演示了由于设计的整形的有效性而增加的传输容量，以及熟练掌握前沿技术的高素质人员。