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

社会依靠光纤网络来承载其巨大的数据需求。最近关于光通信的文献预测，长途光纤通信系统的吞吐量增长将达到一个平台期，估计在未来十年左右，对吞吐量的需求将超过供应。这个问题的长期解决方案是系统地更换所有长途光纤（仅在美国就有数百万英里），以更有效的光纤。这个研究项目探索了一个不那么激进的短期解决方案。它试图回答以下问题：如果通过使用更复杂的信道建模和信号处理，放宽在导出吞吐量平台时所做的限制性假设，系统容量是否可以充分增加以延迟甚至避免昂贵的光纤升级？在这个项目中，开发了信号和噪声通过光纤共传播的完整概率描述，以便统计信号处理方法可以有效地用于这个困难的信道。首先推导了波分复用系统和新兴弹性光网络的光纤系统的概率表示，包括其相关、非平稳和非高斯特性。该研究包括推导出信号和噪声联合过程在长途光纤中传播时的高斯混合模型的每个模式的统计量。所建立的模型可用于对系统性能进行精确的封闭式预测。然后开发优化上述性能指标的信号处理算法和接收器设计。该程序利用了为确定性信号开发的当前模型。重点是生成准确而简单的模型，使它们更普遍地适用于传统和新的光网络。

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.