Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Abstract

Supervision of the physical layer of optical networks is an extremely relevant subject. To detect fiber faults, single-ended solutions such as the Optical Time Domain Reflectometer (OTDR) allow for precise measurements of fault profiles. Combining the OTDR with a signal processing approach for high-dimensional sparse parameter estimation allows for automated and reliable results in reduced time. In this work, a measurement system composed of a Photon-Counting OTDR data acquisition unit and a processing unit based on a Linearized Bregman Iterations algorithm for automatic fault finding is proposed. An in-depth comparative study of the proposed algorithm's fault-finding prowess in the presence of noise is presented. Characteristics such as sensitivity, specificity, processing time, and complexity, are analysed in simulated environments. Real-life measurements that are conducted using the Photon-Counting OTDR subsystem for data acquisition and the Linearized Bregman-based processing unit for automated data analysis demonstrated accurate results. It is concluded that the proposed measurement system is particularly well suited to the task of fault finding. The natural characteristic of the algorithm fosters embedding the solution in digital hardware, allowing for reduced costs and processing time.

Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications

“…A prolific problem that can be cast as a combined 1 / 2 minimization is Trend Break Detection (TBD), which also finds applications in several fields of research [9,10]. In fact, the results presented in [11,12] indicated outstanding performance achieved by the Linearized Bregman Iteration (LBI) when applied to the TBD problem. Moreover, an efficient hardware implementation of the algorithm, with gains in processing time of about two orders of magnitude [12], attests to the prowess of the LBI for trend break detection.…”

“…In such an application, timely trend break detection results are sought so that mobile repair units can be quickly deployed and the downtime of the network can be kept as small as possible so as not to affect the network users greatly [15,16]. Simultaneously, datasets produced by optical fiber monitoring devices can contain several thousands of points [11].In this work, a new methodology to deal with high-dimensional TBD problems within the LBI framework is proposed. This is the profile-splitting method, where, instead of analyzing the profile as a single N-dimensional vector, the algorithm evaluates multiple M-dimensional vectors that, together, compose the original data.…”

“…Even though the gain in timing arising from this approach can be easily demonstrated, two fundamental issues arise.The first issue regards the performance of the algorithm, which must be maintained in order for the method to be valid; otherwise, the method would be equivalent to sacrificing estimation performance for faster results, which could be achieved with different algorithms for even faster processing times [14]. Upholding the unmatched trend break detection prowess of the LBI [11,12] is, therefore, a crucial goal of the profile-splitting method, and the discussion and results are presented in Section 3.The second issue regards the actual implementation of units that process the split-profiles simultaneously and within the same hardware. It is necessary to determine whether the available platforms contain enough resources such that the simultaneity leads to gains in timing as expressive as expected.…”

“…In such an application, timely trend break detection results are sought so that mobile repair units can be quickly deployed and the downtime of the network can be kept as small as possible so as not to affect the network users greatly [15,16]. Simultaneously, datasets produced by optical fiber monitoring devices can contain several thousands of points [11].…”

“…The first issue regards the performance of the algorithm, which must be maintained in order for the method to be valid; otherwise, the method would be equivalent to sacrificing estimation performance for faster results, which could be achieved with different algorithms for even faster processing times [14]. Upholding the unmatched trend break detection prowess of the LBI [11,12] is, therefore, a crucial goal of the profile-splitting method, and the discussion and results are presented in Section 3.…”