一种用于光纤纵向功率剖面监测的块最小均方算法
A Block Least Mean Square Method for Fiber Longitudinal Power Profile Monitoring
AI总结：
本文提出一种块LMS算法，用于高效监测光纤纵向功率剖面，通过频率域关键计算实现低复杂度和低时延的实时处理。
摘要：
我们提出了一种块最小均方（LMS）算法，通过基于接收端的数字数据和相干检测器来监测光纤链路的纵向功率剖面。与基准最小二乘（LS）方法相比，所提算法不需要大规模矩阵求逆或批量处理，因此允许通过重叠保存算法以最小的块大小处理接收到的数据，从而降低复杂度和时延。我们提出了一种高效的实现方法，利用频率域中的关键计算进行随机梯度更新，相较于最先进的监测技术提供了计算节省。我们通过数值模拟在不同场景中测试了该方案。
英文摘要：
We propose a block least mean square (LMS) algorithm to monitor the longitudinal power profile of a fiber-optic link through receiver-based digital data from a coherent detector. Compared to the benchmark least squares (LS) method, the proposed algorithm does not require large matrix inversions or batch processing, thus allowing the received data to be processed in blocks of minimum size by an overlap-save algorithm, reducing complexity and latency. We propose an efficient implementation of the method with a stochastic gradient update leveraging a key computation in the frequency domain, offering computational savings over state-of-the-art monitoring techniques. We test the proposal in different scenarios by means of numerical simulations.
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