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University of British Columbia, Vancouver, Distinguished Lecture, Feb. 27, 2006. Crosstalk and Loop Make-Up Identification for DSL Systems. Dr. Stefano Galli Senior Scientist, Telcordia Technologies [email protected] Talk Outline.

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Crosstalk and loop make up identification for dsl systems

University of British Columbia, Vancouver, Distinguished Lecture, Feb. 27, 2006

Crosstalk and Loop Make-Up Identification for DSL Systems

Dr. Stefano GalliSenior Scientist, Telcordia Technologies [email protected]://

Talk outline
Talk Outline Lecture, Feb. 27, 2006

  • DSL issues

  • DSL Spectrum Management Enablers

    • Crosstalk Identification

    • Loop Make-Up Identification

  • Results on Iterative Algorithms for Optimization

  • Simulation results

Copper impairments

Loop loss Lecture, Feb. 27, 2006

Unshielded drop

and inside wire





EMI radio ingress

Multi-pair cable

Impulse noise

Copper Impairments

  • “POTS” faults

    • grounds, shorts,

    • load coils, balance, dBrn, loop length

  • Loop loss

    • Increases with frequency

    • Bridged tap: up to 10 dB spectral dips

  • Crosstalk

    • Coupling between different systems on different pairs

    • Often most significant noise

    • Multi-pair feeder and distribution cabling

  • Radio ingress noise = Electromagnetic interference (EMI)

    • Narrow band frequency spikes

    • Unshielded drop and inside wire

  • Impulsive noise hits

    • Short bursts (10s of microseconds) of high power noise

    • Long-term (hour) error monitoring

  • Non-linear distortion

    • From phones with no microfilter, some protectors

Managing crosstalk
Managing crosstalk Lecture, Feb. 27, 2006

  • As DSL rollout continues, the problem of crosstalk will become more and more important for at least two reasons:

    • Spectral compatibility issues  necessity of impartial third party for monitoring and resolution

    • Performance issues  necessity of multiuser detection, cancellation, or dynamic management

  • Systems engineered to face worst case xtalk

Dynamic spectrum management dsm

CO-based ADSL Lecture, Feb. 27, 2006




Terminal (RT)
















Dynamic Spectrum Management (DSM)

Today – equal transmit spectra

really bad Crosstalk







Frequency bands

Joint Optimization

Power Back-off

Managing crosstalk1

DSL #1 Lecture, Feb. 27, 2006

DSL #2

DSL #3




Managing crosstalk

  • Crosstalk is NOT random noise – we can control it!

    • Identify crosstalk sources and couplings

  • Spectrum balancing

    • Balance transmit power levels

    • Adapt transmit spectra to optimize performance AND minimize crosstalk


  • Vectoring

    • Real-time signal coordination, crosstalk cancellation


Input data from dsl modems dslams
Input data from DSL Modems & DSLAMs Lecture, Feb. 27, 2006

  • Double-Ended Loop Test (DELT)

    • Requires working DSL service

    • Now: proprietary TL1 & SNMP interfaces

      • Bit rates, SNR Margin, Gross Attenuation, Bit loading spectrum

      • Different formats, often lack accuracy

  • ADSL2 & 2+ Diagnostics (ITU G.992.3 & .5) - products just out

    • Standardized formats, accuracy

    • For each of the 255 subcarriers to 1.1 MHz: Channel Transfer Function H(f), Quiet Line Noise PSD QLN(f), and Signal‑to‑Noise Ratio SNR(f)

    • Aggregate: Line Attenuation, Signal Attenuation, Signal‑to‑Noise Margin, Attainable Net Data Rate, Aggregate Transmit Power

  • Single-ended loop test (SELT), G.selt ITU-T project, vendor implementations

    • Single-ended modem-based loop & noise tests – no service or end-user modem required

  • Extract data from adsl modems
    Extract data from ADSL modems Lecture, Feb. 27, 2006

    ADSL Discrete multi-tone (DMT) modem data:

    - 255 tones

    - Nearly as good as continuous spectra!

    Crosstalk Identification Lecture, Feb. 27, 2006

    SELT Approach

    • It is targeted both to the estimation of the pair-to-pair couplings and to the identification of the source.

    • It is useful for crosstalk cancellation/multiuser detection.

    • Should not only be modem-based: it may be used before providing service, measures crosstalk across all bandwidth.

    • Not much literature on xtalk identification. First papers are recent:

    • Zeng, C. Aldana, A. Salvekar, J. Cioffi, “Crosstalk Identification in DSL Systems”, IEEE JSAC, Aug. 2001.

    • S. Galli, C. Valenti, K. Kerpez, “A Frequency-Domain Approach to Crosstalk Identification in DSL Systems”, IEEE JSAC, Aug. 2001.

    • Traditional approach is in terms of power sums ( Lecture, Feb. 27, 2006sum of the pair-to-pair NEXT coupling powers of the other pairs in the binder group).

    • Engineering is made in terms of the 1% worst case: linear in the log-log scale, 15 dB per decade of frequency.

    Power-Sum Models !

    Worst 25 measured pair-to-pair crosstalk couplings out of 300

    Dark black line = 99% worst-case model

    Crosstalk Identification Algorithm 300

    • Perform a vast measurement campaign of the pair-to-pair couplings on several cables.

    • Create a set of pair-to-pair couplings, choosing them on the basis of a specific criterion  create a dictionary of ptp couplings.

    • Multiply the ptp couplings of the dictionary by the PSDs of all the possible xDSLs (ISDN, ADSL, HDSL, etc.)  create a dictionary of crosstalk PSDs profiles.

    • Measure xtalk PSD and search for the “closest” xtalk PSD profile in the dictionary.

    • Finding the “closest” PSD profile gives us the most likely xtalk source and ptp coupling.

    Vector Choice Methods 300

    • Power: ranking the ptp couplings on the basis of their dB sum across all frequencies.

    • Singular Value Decomposition: choosing only the linear independent vectors in the dictionary.

      Search Methods

    • Correlation: Perform a statistical correlation between measured xtalk PSD and xtalk PSD profiles in the dictionary.

    • Multiple Linear Regression: best suited for the identification of multiple disturbers.

    • Matching Pursuit: mathematical formulation of the problem equivalent to the problem of finding the best sparse representation of a vector on the basis of an overcomplete dictionary.

    Crosstalk ID: Problem Statement 300

    : set of N frequency samples of the measured crosstalk PSD

    caused by un unknown DSL disturber

    : set of N frequency samples of the k-th basis crosstalk PSD

    profile, with 1  k P

    Find the single disturber that generates crosstalk Y given the set of all the

    crosstalk PSD profiles X, i.e. find relationship between Y and X

    Classical regression problem:

    The regression coefficients 300a(k) and b(k) are determined by the condition

    that the sum of the squared residuals S(k) is minimum:

    It is possible to show that the sum of squared residuals can be expressed

    in terms of the correlation coefficient:

    Minimizing sum of squared

    residuals is equivalent to

    finding the maximum

    correlation coefficient

    vector containing the measured crosstalk PSD from unknown 300

    disturbers across all N frequencies

    full rank NxP matrix containing all the PSD profiles

    vector of weighting coefficients

    vector containing the residuals over all the frequency points

    • N< 300<P, matrix  constitutes an overcomplete set of vectors for N

    • Number of disturbers M small, M<<P

    Problem: find an optimal sparse representation of a

    vector from an overcomplete set of vectors.

    No SVD 300

    (P = 800)


    ( = 8)

    1) ISDN



    2) HDSL



    3) T1



    4) ADSL Dn



    5) SDSL 400



    6) SDSL 1040



    7) SDSL 1552



    8) HDSL2 Up






    ID rate (%)



    SVD allows to drastically reduce computational complexity.

    Loop response estimation

    22 Gauge 300

    650 ft

    22 Gauge

    22 Gauge

    813 ft

    1400 ft

    Loop Response Estimation

    • TDR wideband reflectometry signals estimate bridged taps, segment lengths and gauges

    • Entire DSL band spectral response

      • Better than just one frequency or one length number

      • Determine Bridged Tap effects

    • Example: Actual loop Vs. 1328 ft equivalent 26 gauge (EWL)

    Necessity of Loop Qualification 300

    • Not all local loops can support DSL technology

    • Before DSL can be deployed, local loops must be tested to see whether they can support it or not

    • This may be obtained on the basis of a detailed loop characterization

    Detailed loop characterization is difficult to obtain because:

    1) information kept for POTS service was not detailed

    2) loop records are often on paper

    3) records are often wrong

     It is necessary to perform measurements 

    Types of Loop Qualification 300

    • Single-ended testing:

    • Requires test equipment at the central office only

    • Measures can be performed at the Central Office without involving on-site technicians

    • Double-ended testing:

    • Requires equipment at both ends of the loop

    • Involves dispatching a technician to the customer’s location

    • Transfer function easily estimated

    • Single-ended testing is more complicated but:

    • It is less expensive

    • It may allow us to unveil the loop make-up, not only the transfer function  “loop identification” implies loop qualification

    Loop Make-Up Identification 300







    Telco DSL CO

    Estimate loop make-up stick diagram from single-ended CO-based TDR measurement






    26 gauge

    24 gauge

    24 gauge

    22 gauge

    • The test equipment transmits a set of “ad-hoc” designed signals

    • When a signal travels on a loop and encounters medium discontinuities (gauge changes, bridged taps, end of line) part of the signal is reflected back, i.e. an echo is generated.

    • Unwanted spurious echoes are also always present

    • The test equipment will process the received echoes

    Observation of the superposition of all the echoes 300

    (Test signal: 200 ns Square Pulse, 1 V amplitude)

    Echo Modeling

    Echo Modeling 300

    • The following unknown quantities must be estimated:

    • Time of arrival of the echo: ti

    • Amplitude of the echo: ai

    • Waveform of the echo (shape): e(i)(t)

    • Number of non-negligible echoes N

    • Problems:

    • Some echoes are spurious and some are not

    • Some echoes overlap

    Echo Modeling 300

    • The shape of echo (real or spurious) generated at a discontinuity depends on the following quantities:

    • The insertion loss of the echo path:

    • The reflection coefficient:

    • The transmission coefficient: t(f)= 1 + r(f)

    Echo Modeling 300

    The reflection coefficient for the spurious echoes:

    Gauge Changes:

    Bridged Taps:

    Traditional Signal Processing Approach 300

    • The problem of detecting and resolving signals generated by D sources has been usually addressed assuming the availability of an array of M>D sensors.

    • It is a combined detection/estimation problem: determine the number of sources and then estimate their location in time.

    • In our case the sources are the discontinuities, the signals are the echoes and the location in time is the position of the discontinuity.

    Traditional Signal Processing Approach 300

    Problem: Here we have only one sensor!!!

    Let’s try to turn our single sensor case into a multi-sensor one and use MUSIC, ESPRIT, or WSF.

    Traditional Signal Processing Approach 300

    Similar to the multi-sensor problem but now the array manifold depends on the shape of the echo

    • Problems:

      • The manifold S requires the knowledge of the shape of the echo and that all the echoes have the same shapes.

      • The real and spurious echoes would still be undistinguishable from each other.

    Not much literature on Loop Make-Up identification. 300First papers are recent:

    • S. Galli, D. L. Waring, “Loop Make-up Identification via Single Ended Testing: Beyond Mere Loop Qualification”, IEEE J. Select. Areas Commun., vol. 20, no. 5, June 2002.

    • T. Bostoen, P. Boets, M. Zekri, L. Van Biesen, T. Pollet, and D. Rabijns, "Estimation of the Transfer Function of a Subscriber Loop by Means of a One-Port Scattering Parameter Measurement at the Central Office," IEEE J. Select. Areas Commun., vol. 20, no. 5, June 2002.

    A Novel Step-by-Step Maximum Likelihood Technique 300

    The identification process is based on analyzing TDR measurements in such a way that the measurements are successively mapped to gradually augmented loop make-up topologies until the error between the measured TDR trace and the simulated TDR waveform of a set of hypothesized loop topologies becomes sufficiently small.

    S. Galli, K. Kerpez, "Single-Ended Loop Make-Up Identification - Part 1 and 2," IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 2, April 2006.

    A Novel Step-by-Step Maximum Likelihood Technique 300

    1) Hypothesize all “sensible” topologies and generate corresponding waveform according to echo model

    2) Choose topology whose waveform best matches measured data, and identify discontinuity

    3) Augment chosen topology using auxiliary topologies (infinite length), generate corresponding waveform, and subtract it from measured data to obtain a de-embedded TDR trace

    4) Identify the next discontinuity

    5) Go to 2 using de-embedded trace as measured data until last echo is found

    • Rationale:

    • Exploit the “deterministic” nature of the twisted-pair.

    • The echoes from near discontinuities “hide” the echoes from far discontinuities  de-embedding

    Previous loop estimate 300























    26 AWG

    26 AWG

    24 AWG

    24 AWG

    24 AWG

    24 AWG

    24 AWG

    24 AWG







    Current loop estimate

    Enhancement: Multiple Estimate Path Search

    State space

    Reduced- state

    Viterbi estimation

    Experimental results: EWL error 300

    19 loops representing the variety at a CO. Loops picked so that 5%, 10%, ..., 95% of all loops at the wire center were shorter. Loops include bridged tap, gauge change, etc.

    Simulation results

    Different actual 300


    crosstalk coupling



    Static spectrum management

    Simulation results

    Presented at the IEEE ICC 2005 conference

    Conclusions 300

    • Crosstalk is not random noise, we can control it

    • Efficient optimization algorithms need:

      • Crosstalk identification

      • Loop make-up identification

    • With optimization, bit rates increase several times

    The probability histogram of total bridged tap lengths, measured in the 1983 loop survey (excluding zero lengths)

    Cable gauge statistics from the 1983 and the 1987-90 Bellcore loop surveys. In the 1987-90 Bellcore loop surveys, only 0.1% of cabling overall was 19 gauge, so 19 gauge is omitted from the table

    Broadband Test Head prototype architecture Bellcore loop surveys. In the 1987-90 Bellcore loop surveys, only 0.1% of cabling overall was 19 gauge, so 19 gauge is omitted from the table

    Schematic diagram of our differential TDR Bellcore loop surveys. In the 1987-90 Bellcore loop surveys, only 0.1% of cabling overall was 19 gauge, so 19 gauge is omitted from the table