WDM-PON Monitoring With Tunable Photon Counting OTDR

Amaral, Gustavo C.; Herrera, Luis E.; Vitoreti, Douglas; Temporão, Guilherme P.; Urban, Patryk J.; Weid, J. P. von der

doi:10.1109/lpt.2014.2320871

Cited by 27 publications

(16 citation statements)

References 12 publications

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“…The OTDR provides the fiber profile by associating the measured data to the pulse's round-trip time, which, combined with the knowledge of the fiber's refractive index and the speed of light, can be translated into distance. The fiber profile is, thus, a descending line in 6 logarithmic scale with a slope equal to the fiber's attenuation coefficient [2], [4], [34]. Any event which causes optical power loss is interpreted accordingly, i.e., the aforementioned effects of fiber bending, fiber breaking, imperfect fiber splices, and corrupted connectors can be identified by an abrupt level shift in the signal profile.…”

Section: Otdr and Lbi -Basic Conceptsmentioning

confidence: 99%

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Lunglmayr¹,

Amaral²

2019

IEEE Trans. Instrum. Meas.

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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.

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Section: Otdr and Lbi -Basic Conceptsmentioning

confidence: 99%

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Lunglmayr¹,

Amaral²

2019

IEEE Trans. Instrum. Meas.

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“…Remote detection of faults in optical fibers is an essential tool for the robust operation of modern optical networks, which compose the physical layer upon which the current high-rate telecommunication services are built. Single-ended solutions, such as Optical Time Domain Reflectometry (OTDR) [23], are preferred since the optical network manager can simultaneously feed the fiber with data signals and also probe it for faults [24]. The fundamental working principle of the OTDR is to send a probing pulse into the optical fiber and acquire the backscattered light as a function of time; this is, then, translated into distance with the knowledge of the average speed of light in the fiber.…”

Section: Application To Fiber Fault Detectionmentioning

confidence: 99%

Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications

2020

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Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length N of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to N. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.Electronics 2020, 9, 423 2 of 18 quadratic with respect to the dataset length N-which is demonstrated in Section 2-and the timing necessary for the algorithm to converge becomes eventually impracticable. Even though the TBD problem has a broad presence across different scientific fields, the application focus, in this document, will be given to fault detection in optical fiber profiles, in which trend breaks are associated with such faults [13,14]. 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. In Section 2, the combined 1 / 2 minimization cast as a TBD problem is presented, as well as the structure of the profile-splitting method. 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 r...

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“…Presently, different techniques for optical fiber characterization are available and standardized such as the Optical Time Domain Reflectometry (OTDR) and Frequency Domain Reflectometry (OFDR) [2]. In any case, embedding any of these fiber-monitoring techniques in Passive Optical Networks (PON) systems remains a challenge in terms of technology and cost-effectiveness not to mention the in-service monitoring paradigm [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Low-Frequency Tone Sweep Method for In-Service Fault Location in Subcarrier Multiplexed Optical Fiber Networks

Amaral

Baldivieso

Garcia

et al. 2017

J. Lightwave Technol.

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We demonstrate an optical fiber fault location method based on the frequency response of the modulated fiber optical backscattered signal in a steady state low-frequency step regime. Careful calibration and measurement allows for the reconstruction of the fiber transfer function, which, associated to its mathematical model, is capable of extracting the fiber characteristics. The technique is capable of identifying non-reflective fault events in an optical fiber link and is perfectly compatible with previous methods that focus on the reflective events. The fact that the recuperation of the complex signal is performed in the frequency domain and not via a Fourier Transform enables the measurements to overcome the spatial resolution limitation of Fourier Transform incoherent-OFDR measurements even with frequency sweep ranges down to 100-100000 Hz. This result is backed up by a less than 10 meters difference in fault location when compared to standard OTDR measurements.

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WDM-PON Monitoring With Tunable Photon Counting OTDR

Cited by 27 publications

References 12 publications

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications

A Low-Frequency Tone Sweep Method for In-Service Fault Location in Subcarrier Multiplexed Optical Fiber Networks

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