High-Dynamic and High-resolution Automatic Photon Counting OTDR for Optical Fiber Network Monitoring

Calliari, Felipe; Herrera, Luis E.; Weid, J. P. von der; Amaral, Gustavo C.

doi:10.5220/0006643900820090

Cited by 12 publications

(9 citation statements)

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“…An analysis based on the computational complexity will also be drawn in the next section, since it is of the utmost importance when adapting the algorithm to an embedded processing unit, one of the sought for goals of automatic fault finding [50].…”

Section: B Controlled Test Resultsmentioning

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: B Controlled Test Resultsmentioning

confidence: 99%

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Lunglmayr¹,

Amaral²

2019

IEEE Trans. Instrum. Meas.

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“…Furthermore, it can be shown that even with an exceedingly high value of λ, the results will scarcely contain a single non-zero β entry where the break can be expected, but rather, a cluster of non-zero β entries around the position of the break. Since, in many applications [11,13,19], it is important that the procedure returns a single value for the positions of breaks, techniques such as cluster analysis [20] or extensive searches on the reduced detected cluster subspace [21,22] can be employed to narrow down the positions. In [11], a procedure involving the detection of the highest identified peaks was put forth with high quality results and minimum computational effort, so this methodology is also considered here.…”

Section: The Lbi Algorithm Applied To the Tbd Problemmentioning

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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“…In 1980, photon-counting OTDR (PC-OTDR) based on the Geiger-mode single photon detector (SPD) was proposed and demonstrated [9]. Compared with the conventional OTDR, PC-OTDR can offer advantages of higher spatial resolution and larger dynamic range [10] [11]. Especially, with the development of single photon detection technology, Q. Zhao et al obtained a spatial resolution of 4 mm with superconducting nanowire single-photon detectors (SNSPDs) [12], J. Hu et al exhibited a dynamic range of 22 dB m with 6.0 cm spatial resolution at the end of 2 km standard single-mode fiber [13], and G.-L. Shentu et al achieved a dynamic range of 42.19 dB with a spatial resolution of 10 cm with an ultra-low noise up-conversion SPD [14].…”

Section: Introductionmentioning

confidence: 99%

High Dynamic Range Externally Time-Gated Photon Counting Optical Time-Domain Reflectometry

Song

Zhou

et al. 2019

J. Lightwave Technol.

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Single photon detector (SPD) has a maximum count rate due to its dead time, which results in that the dynamic range of photon counting optical time-domain reflectometry (PC-OTDR) decreases with the length of monitored fiber. To further improve the dynamic range of PC-OTDR, we propose and demonstrate an externally time-gated scheme. The externally time-gated scheme is realized by using a high-speed optical switch, i.e. a Mach-Zehnder interferometer, to modulate the back-propagation optical signal, and to allow that only a certain segment of the fiber is monitored by the SPD. The feasibility of proposed scheme is first examined with theoretical analysis and simulation; then we experimentally demonstrate it with our experimental PC-OTDR testbed operating at 800 nm wavelength band. In our studies, a dynamic range of 30.0 dB is achieved in a 70 meters long PC-OTDR system with 50 ns external gates, corresponding to an improvement of 11.0 dB in dynamic range comparing with no gating operation. Furthermore, with the improved dynamic range, a successful identification of a 0.37 dB loss event is detected with 30-seconds accumulation, which could not be identified without gating operation. Our scheme paves an avenue for developing PC-OTDR systems with high dynamic range.

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High-Dynamic and High-resolution Automatic Photon Counting OTDR for Optical Fiber Network Monitoring

Cited by 12 publications

References 0 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

High Dynamic Range Externally Time-Gated Photon Counting Optical Time-Domain Reflectometry

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