A Block Least Mean Square Method for Fiber Longitudinal Power Profile Monitoring

TL;DR

This paper introduces a block LMS algorithm for fiber power profile monitoring that reduces computational complexity and latency compared to traditional methods, enabling real-time processing with efficient frequency domain updates.

Contribution

The paper presents a novel block LMS algorithm that avoids large matrix inversions and batch processing, improving efficiency for fiber power profile monitoring.

Findings

Reduces computational complexity and latency.

Enables real-time processing with block updates.

Demonstrates effectiveness through numerical simulations.

Abstract

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.