Line Topology Identification Using Multiobjective Evolutionary Computation

Sales, Claudomiro; Rodrigues, Roberto M.; Lindqvist, Fredrik; Costa, João Crisóstomo Weyl Albuquerque; Klautau, Aldebaro; Ericson, Klas; Riu, Jaume Rius i; Börjesson, Per Ola

doi:10.1109/tim.2009.2025991

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…The different representative short links are described in Table III in the next section. The prior information is obtained through the usage of LTI [13], which identifies the topology of a loop (number of sections, length and gauges) based on one-port measurements.…”

Section: General Methodologymentioning

confidence: 99%

“…In this tool is possible to measure real-time noise power estimation using embedded software in the DSL Access Multiplexer (DSLAM) side. This solution has a possible application in mobile backhaul network of urban cities with a knowing topology and/or having loop topology identification (LTI) [13] as the prior knowledge. Another important point is that the information acquired can be used to manage the backhaul quality of service for indoor solutions, to develop new techniques to mitigate noise, and to relate packet loss and jitter with the interference effect.…”

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confidence: 99%

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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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Section: General Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

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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“…GA converges to a solution by generating subsequent variables from parent variables that best-fit a chosen metric (fitness function). In this effort the GA variables are listed below and different from those in [12]. Gene : Length of loop [kft], Chromosome C: Computed FDR output mapped to a gene, Population P: A set of FDR outputs, each corresponding to a gene that represents one generation and a Fitness function (FF) which is the MSE between simulated (ideally measured) and computed data.…”

Section: Loop Topology Estimation and Verification Using Frequenmentioning

confidence: 99%

“…In this effort (Section III) we employ actual measured data from a 25 pair physical cable, and introduce preprocessing of measured data that provides an initial inaccurate estimate as an input to the FDR optimization for further refinement. Also introduced is an Evolutionary computation based on a biologically inspired natural selection process that computes using GA [12], and compared with the same measured data in section III. Further to improve the resolution, linear m-sequences that possess good autocorrelation and accurate ranging properties using offsets [11] are used in CTDR.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for physical and power line loop topology estimation using a broadband modem

Ravishankar¹,

Arjun

2016

2016 IEEE Radio and Wireless Symposium (RWS)

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A two-step (hybrid) procedure to extract open port physical line topology to simulate channel models is discussed here. An initial approximate estimate of the loop is obtained from Frequency Domain Reflectometry (FDR) measured data itself or by a Correlation Time Domain Reflectometry (CTDR). Thereafter an optimization algorithm is used to predict a more accurate loop topology, by defining an objective function and iterating based on the measured FDR data, and the computed frequency domain echo of the initial estimate. Verification for this optimization scheme was done by actual FDR measurement Tests on physical loops. Additionally introduced is a Genetic Algorithm (GA) procedure to verify the measurement data for plain lines. Simulations are presented for a typical physical power lines using the hybrid method. No prior knowledge of the network topology is required. Index Terms -Digital subscriber line (DSL), Power Line Communication (PLC), Frequency domain Reflectometry (FDR), Correlation time domain Reflectometry (CTDR), Loop qualification.

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“…The original implementation of the TIMEC presented in [16], kindly provided by the authors, was used. The original implementation of the TIMEC presented in [16], kindly provided by the authors, was used.…”

Section: Baseline Comparisonmentioning

confidence: 99%

A wavelet‐based expert system for digital subscriber line topology identification

Lima

Klautau

Costa

et al. 2014

Int J Communication

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This work proposes a new method for automatically identifying topologies of lines with one or more sections in a telephone network. The method is based on the examination of both impulse response and time-domain reflectometry trace of a line under test. They are analyzed using a method based on the wavelet transform that identifies and extracts features that contain information about the line topology. Those features are interpreted by an expert system composed of three sequential modules that estimate, respectively, the type of line makeup (serial or bridge tap), the lengths of the line sections, and the corresponding cable type, which are the parameters that completely identify the topology according to the assumed model. A thorough comparison with two state-of-the-art methods is also presented using several twisted-pair copper cables. The results show that the proposed method provides good accuracy with respect to topology identification at low computational cost.

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Line Topology Identification Using Multiobjective Evolutionary Computation

Cited by 17 publications

References 11 publications

Noise estimation in DSL systems using linear regression

Noise estimation in DSL systems using linear regression

A hybrid method for physical and power line loop topology estimation using a broadband modem

A wavelet‐based expert system for digital subscriber line topology identification

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