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CIF: Small: Statistical Approach to Signal Processing for Long-Haul Fiber-Optic Communication Sytems

CIF：小型：长距离光纤通信系统信号处理的统计方法

基本信息

批准号：
1422871
负责人：
Maite Brandt-Pearce
金额：
$ 36.84万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422871&HistoricalAwards=false
关键词：
CIF Small Statistical Approach Signal

项目摘要

Society depends on fiber-optic networks to carry its huge data demands. Recent literature on optical communications has predicted a plateau in the throughput increase of long-haul fiber-optic communication systems, estimating that the demand for throughput will exceed supply within the next ten years or so. The long term solution to this problem is the systematic replacement of all long-haul fiber (millions of miles just in the US) to more effective fibers. This research project explores a less drastic shorter-term solution. It seeks to answer the following question: if limiting assumptions made in deriving this throughput plateau are relaxed, by using more sophisticated channel modeling and signal processing, can the system capacity be increased sufficiently to delay or even obviate the costly fiber upgrade? In this project a full probabilistic description of the signal and noise co-propagation through optical fibers is developed so that statistical signal processing approaches can be effectively used on this difficult channel. A probabilistic representation of the fiber-optic system including its correlated, nonstationary, and nonGaussian qualities for wavelength division multiplexed systems and emerging elastic optical networks is first derived. The research includes the derivation of the statistics of each mode of a Gaussian mixture model for the joint signal and noise processes as they propagate through a long-haul fiber. The models developed are used to derive accurate closed-form predictions of the system performance. Signal processing algorithms and receiver designs that optimize the above performance metrics are then developed. The program leverages current models developed for deterministic signals. The focus is in generating accurate yet simple models to make them more generally applicable to both legacy and new optical networks.

社会依赖光纤网络来承载其巨大的数据需求。最近有关光通信的文献预测，长途光纤通信系统的吞吐量增长将进入平台期，估计在未来10年左右的时间里，吞吐量将供不应求。这个问题的长期解决方案是系统地将所有长途光纤(仅在美国就有数百万英里)更换为更有效的光纤。这项研究项目探索了一种不那么激进的短期解决方案。它试图回答以下问题：如果放宽在推导吞吐量平台时所做的限制假设，通过使用更复杂的信道建模和信号处理，系统容量能否充分增加，以延迟甚至避免昂贵的光纤升级？在本项目中，我们建立了信号和噪声在光纤中共同传播的全概率描述，以便在这一困难的信道上有效地使用统计信号处理方法。首先推导了波分复用系统和新兴弹性光网络中光纤系统的概率表示，包括光纤系统的相关、非平稳和非高斯性。这项研究包括推导联合信号和噪声过程在长距离光纤中传播时的高斯混合模型的每个模式的统计量。所建立的模型被用来获得系统性能的精确的闭式预测。然后开发了优化上述性能指标的信号处理算法和接收器设计。该计划利用了目前为确定性信号开发的模型。重点是生成准确而简单的模型，使其更普遍地适用于传统和新的光网络。