Binder Identification by Means of Phantom Measurements

Neus, Carine; Foubert, Wim; Biesen, Leo Van; Rolain, Yves; Maes, Jochen

doi:10.1109/tim.2011.2115452

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…A previous work [5] revealed evidences of TPs identification in the same cable using phantom measurements, which will be discussed in this section. The so-called phantom circuit ( Figure 1) is obtained using one TP (two wires) for transmitting common mode signals traveling in the positive direction and other TP (two wires) for common mode signals traveling in the negative direction.…”

Section: Phantom-mode Signalmentioning

confidence: 72%

“…The mixture of all gaussian distributions is expressed by the Equation 5. The algorithm stops when the summation of log-likelihood for all samples in the Equation 5 is not higher than a configured threshold.…”

Section: K-meansmentioning

confidence: 99%

“…This method takes considerable time to produce results (a few days are required to identify closer TPs) and no information is given about the binder length shared by the TPs. In [5], a study reveals the possibility of binder identification via interpretation of line parameters such as the input impedance and reflection of the phantom circuit composed by two TPs. It is pointed out that the phantom circuit composed by TPs in different binders presents high impedance, which is an evidence for the identification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clustering Strategies for Binder Identification Using Phantom Measurements

Santos¹,

Sales²,

Lima³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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This paper proposes an automatic method for the identification of twisted pairs sharing the same binder, based on the analysis of phantom circuit measurements. This type of circuit is often used for improving data transmission rates in communication systems, but in this work, phantoming is used to reveal if a 4-wire loop composed by two twisted pairs are close enough and well balanced in order to be considered in the same binder. The identification is done via application of two pattern recognition techniques, K-means and Gaussian Mixture Model, on S 11 parameter obtained from the phantom-mode measurement of two twisted pairs. This paper also presents an automatic method to labeling the clusters and a method to estimate the length of the two twisted pairs that share the same binder, using Time-Domain Reflectometry analysis. Laboratory results confirm the accuracy of the proposed methods.

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Section: Phantom-mode Signalmentioning

confidence: 72%

Section: K-meansmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Strategies for Binder Identification Using Phantom Measurements

Santos¹,

Sales²,

Lima³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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show abstract

“…This method takes considerable time to produce results (a few days are required to identify closer TPs) and no information is given about the binder length shared by the TPs. In [7], a study is presented revealing the possibility of binder identification via interpretation of line parameters such as the input impedance and reflection of the phantom circuit composed by two TPs which are intended to be identified. It is pointed out that the phantom circuit composed by TPs in different binders presents high impedance, which is an evidence for the identification.…”

Section: Introductionmentioning

confidence: 99%

Clustering Strategies applied to Binder Identification using Phantom Measurements

Fertner¹,

Costa²,

Santos³

et al. 2015

Anais De XXXIII Simpósio Brasileiro De Telecomunicações

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This paper proposes a method for the identification of TPs sharing the same binder, based on the analysis of phantom circuit measurements. Herein, phantoming is used to reveal if a 4-wire loop composed by two TPs are close enough in order to be considered in the same binder. K-means and Gaussian Mixture Model are evaluated on S11 parameter features obtained from the phantom-mode measurement of two TPs. Also, an automatic method to labelling the clusters and a method to estimate the length which two TPs share the same binder are briefly presented. Laboratory results confirm the accuracy of the methods.

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Noise estimation in DSL systems using linear regression

Farias

Borges

Monteiro

et al. 2013

2013 International Conference on Advanced Technologies for Communications (ATC 2013)

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The Digital Subscriber Line (DSL) systems performance tightly depends on noise interference. The users (lines) in the binder create mutual interference (crosstalk), therefore decreasing the rates of all users. Crosstalk noise is more predominant in DSL and its major performance bottleneck is to limit high-speed data rate. This study proposes a new real-time monitoring methodology for noise estimation based on Management Information Base (MIB) metrics. Linear regression is used for fitting, in which input parameters are MIBs and the output is the estimated noise power. The results confirm the possibility of estimating noise with a general equation. Moreover, it is showing the result improvement if a Loop Topology Identification tool is used as prior knowledge.

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Binder Identification by Means of Phantom Measurements

Cited by 6 publications

References 13 publications

Clustering Strategies for Binder Identification Using Phantom Measurements

Clustering Strategies for Binder Identification Using Phantom Measurements

Clustering Strategies applied to Binder Identification using Phantom Measurements

Noise estimation in DSL systems using linear regression

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