Source: http://www.google.com/patents/US7809116?ie=ISO-8859-1
Timestamp: 2015-03-31 16:16:20
Document Index: 201942403

Matched Legal Cases: ['Application No. 06710513', 'Application No. 04801291', 'Application No. 2004298117', 'Application No. 200680006774', 'Application No. 200680027968', 'Application No. 200680027968', 'Application No. 200480041373', 'Application No. 200480041373']

Patent US7809116 - DSL system estimation including known DSL line scanning and bad splice ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsEstimates of a communication system configuration, such as a DSL system, are based on operational data collected from a network element management system, protocol, users and/or the like. The operational data collected from the system can include performance-characterizing operational data that typically...http://www.google.com/patents/US7809116?utm_source=gb-gplus-sharePatent US7809116 - DSL system estimation including known DSL line scanning and bad splice detection capabilityAdvanced Patent SearchPublication numberUS7809116 B2Publication typeGrantApplication numberUS 11/069,159Publication dateOct 5, 2010Filing dateMar 1, 2005Priority dateDec 7, 2003Fee statusPaidAlso published asCN101133632A, CN101133632B, EP1875729A1, US20060098725, WO2006092730A1Publication number069159, 11069159, US 7809116 B2, US 7809116B2, US-B2-7809116, US7809116 B2, US7809116B2InventorsWonjong Rhee, Mark Harold Brady, John M. CioffiOriginal AssigneeAdaptive Spectrum And Signal Alignment, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (83), Non-Patent Citations (25), Referenced by (12), Classifications (44), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetDSL system estimation including known DSL line scanning and bad splice detection capability
US 7809116 B2Abstract
This application is a continuation-in-part of U.S. Ser. No. 10/817,128 filed on Apr. 2, 2004 now U.S. Pat. No. 7,302,379, entitled DSL SYSTEM ESTIMATION AND PARAMETER RECOMMENDATION, which claims the benefit of priority under 35 U.S.C. �119(e) of U.S. Provisional No. 60/527,853 filed on Dec. 7, 2003, entitled DYNAMIC MANAGEMENT OF COMMUNICATION SYSTEM, the disclosures of which are incorporated herein by reference in their entirety for all purposes.
�xDSL� and �DSL� are terms used to generally refer to digital subscriber line equipment and services, including packet-based architectures, such as ADSL, HDSL, SDSL, SHDSL, IDSL VDSL and RADSL. DSL technologies can provide extremely high bandwidth over embedded twisted pair, copper cable plant. DSL technologies offer great potential for bandwidth-intensive applications.
DSL services are much more dependent on line conditions (for example, the length, quality and environment of the copper loop) than traditional telephone services, which typically use a bandwidth including frequencies up to about 4 kilohertz compared to DSL services which utilize a bandwidth including frequencies sometimes over 1 MHz. While some local loops are in great condition for implementing DSL (for example, having short to moderate lengths with minimal bridged taps and splices) many local loops are not as suitable. For example, local loop length varies widely. Moreover, the wire gauge for a local loop may not be consistent over the length of the loop, having two or more different gauges spliced together. Still further, many existing local loops have one or more bridged taps (a length of wire pair that is connected to a loop at one end and is unconnected or poorly terminated at the other end), and some local loops have bad splices (for example, a splice that is loosely connected). This type of line information (for example, wire gauge information, bridged-tap information, segment information, bad splice information and load coil information) is important to the evaluation of DSL systems and configurations. Another important class of line conditions is the noise measured on the line, which can be caused by radiation from other DSLs (�crosstalk�), radio ingress of AM or amateur radio stations, thermal noises in the line or receiver analog components, various appliances at the home, electronic equipment in the loop plant or at the central office. These types of noises can vary from time to time and be relatively stationary, impulsive or a combination of both. This type of information also can be important for the evaluation of DSL systems and configurations.
Another embodiment of the present invention is shown in FIG. 3B. A DSL optimizer 365 operates on and/or in connection with a DSLAM 385 or other DSL system component, either or both of which may be on the premises 395 of a telecommunication company (a �telco�). The DSL optimizer 365 includes a data collection and analysis module 380, which can collect, assemble, condition, manipulate and supply operational data for and to the DSL optimizer 365. Module 380 can be a computer such as a PC or the like. Data from module 380 is supplied to a DSM server 370 for analysis (for example, loop configuration estimation, bad splice detection, bad splice location, etc.). Information also may be available from a library or database 375 that may be related or unrelated to the telco. A profile selector 390 may be used to select and implement profiles used during scanning to create, maintain and update any libraries and/or databases used in estimating loop configurations, etc. Profiles may be selected under the control of the DSM server 370 or by any other suitable manner, as will be appreciated by those skilled in the art. Profiles selected by selector 390 are implemented in the DSLAM 385 and/or any other appropriate DSL system component equipment. Such equipment is coupled to DSL equipment such as customer premises equipment 399. The system of FIG. 3B can operate in ways analogous to the system of FIG. 3A, as will be appreciated by those skilled in the art, though differences are achievable while still implementing embodiments of the present invention.
Line 700 shows the �notching� effect of bridged taps on the channel characteristic. Bridged taps are twisted pairs that are connected to some point of the loops and left unterminated at the other end. Due to the signal's attenuation and the resulting notched channel characteristic, bridged taps cause rate loss and severe inter-symbol interference in DSL frequencies. Unfortunately, for the largest part of the telephone network, existing databases are not always accurate, and it is impossible to know the exact location of the bridged taps. In North America, roughly two thirds of the loops have bridged taps, and of these, half have two or more taps. While operators have been developing bridged tap location methods, it is expected that many DSL loops will retain their bridged taps. For systems in operation, the channel is continuously measured and its transfer function is estimated in order to calculate modulation parameters and guarantee reliable operation.
(where Gap in dB is (9.5+TSNRM�CODEGAIN) and SNR[n]≈10GaP/10�2(2B[n])−1 is reported in ADSL2 modems and is computable from past or current reported bit distributions B[n] in ADSL1 modems). The SNR may be computed or inferred using initial PSD, Hlog and/or QLN. The PSD[n]=REFPSD+G[n] (where G[n] is the known or estimated gains table value in dB), and REFPSD=NOMPSD−PCB. Since G[n] usually satisfies −2.5 dB<G[n]<2.5 dB in ADSL1 modems, but might not be reported, G[n] can be estimated by looking for B[n] table changes, usually being near −2.5 dB on the tone with higher number of bits between two adjacent tones and usually near +2.5 dB on the tone with lower number of bits between two adjacent tones.
Some embodiments of the present invention use various operational data that can be collected by choosing different line profiles composed of different control information, where the line profiles can be chosen and controlled by a controller (such as a DSL optimizer and/or DSM server) and implemented via a DSLAM or other suitable means. A line profile specifies one or more restrictions (control information) imposed on the associated DSL line, such as (without limitation) specifications and/or restrictions regarding minimum data rates, planned data rates, maximum data rates, transmit power, maximum power spectrum density, carrier mask, maximum-additional SNR margin settings (noise margins), target SNR margin settings (noise margins), minimum SNR margin settings (noise margins) and/or FEC (forward error correction) parameters, including the INP and DELAY settings of ADSL2. Other profile criteria will be apparent to those skilled in the art. The operational data for each line profile can be collected after the training of the ATU-R modem with the line profile. The process of using multiple line profiles with multiple trainings and data collections for the purpose of collecting diverse operational data is referred to as �scanning� in connection with embodiments of the present invention. Scanning can be used for various purposes including Hlog estimation.
FIG. 5�A carrier mask having only one data bearing tone f is selected 510 (given, for example, in either Hz or as a DSL tone number), the attenuation ATN(f) is obtained 520 and this can be repeated 530 as appropriate. ATN(f) is plotted 540 and any effects that are undesirable can be removed 550. Hlog, QLN, etc. can then be calculated 560 and recommendations made as to operational modes of users and/or other parts of the communication system 570. The reported downstream attenuation for each f selected is close to or the same as the Hlog value of that one active tone (providing Hlog values for any tones used and generating an approximation of the transfer function plot). And/or FIG. 6�A carrier mask is selected at 610 which includes a group of tones (using, for example, the CARMASK function of ADSL1 or ADSL2) between fmin, and fmax (fmin may either be the lowest frequency in the selected band or the lowest data bearing frequency, if the lowest frequency in the band is non-data bearing). ATN(fmin) is obtained at 620 and additional bands may be tested 630, as appropriate. The values of are plotted 640 and any undesirable effects removed 650. Hlog, QLN, etc. can then be calculated 660 and recommendations made as to operational modes of users and/or other parts of the communication system 670. In this method, the downstream attenuation is used to estimate Hlog of the tones near fmin. In most situations, the lowest frequency in the modem-selected set of tones is fmin and thus it the estimation of ATN(fmin) in step 620 is generally quite accurate. By using a variety of values for fmin and repeating the training, the Hlog of the whole bandwidth can be well estimated.
Other embodiments of the present invention permit channel identification using estimates of various parameters available from twisted-pair binder channels in DSL systems. Following are examples of such channel identification for lines with zero to two bridged taps using two types of data�reported ADSL1-standard MIB information only (that is, operational data available from ADSL1 compliant systems and equipment without special data protocols, also referred to as �ASM data� and/or �ASM information� herein) and special data and/or information sets, examples of which are described in more detail below. Examples of ASM information include, but are not limited to, the SBC �ADEPT� and �BBT� data, Telecom Italia's CANTO data, Softbank Broadband's �artificial intelligence� and Korea Telecom's ATLAS system.
Estimation based on noise-dependent parameters inherently necessitates noise estimation. To avoid this additional estimation of noise, some embodiments of the present invention use data that is generally noise-independent. Moreover, since the particular mechanics of the computation used by each modem and DSLAM may not be well understood, the present invention can employ self-configuring methods that avoid the need for �reverse engineering� of each hardware element to approximate the algorithms used therein.
Channel identification (that is, identifying a likely loop configuration and/or its operational characteristics) then is performed by searching through the database for a loop configuration that has reported attenuation values closest to the observed attenuation values for the modem type and the set of line profiles employed. �Closeness� in matching one or more candidate loop configurations to reported data may be defined in various ways, as will be appreciated by those skilled in the art. Illustrative examples and discussion that follow will illuminate this concept.
Some of the data might be missing due to incomplete data collection. In this situation, the data collection can be repeated or the corresponding data fields can be ignored. Define B=[b1 b2 . . . bN]. Clearly, B is an L�N matrix representing at least part of database 348. In some cases, x might perfectly match one of the columns in B. Where no exact match is found, an interpolation between different database entries can be used to generate an estimate. According to one estimation method, define θ to be a column vector with N elements, where θ is a weighting vector used to linearly combine multiple columns of B to form an estimation. The following example illustrates the use of θ in performing an interpolation:
1. For the N columns of B, find the distance of each column from x, where the distance is defined as ∥x-bn∥2 for the nth column. 2. Choose three columns of B that give three smallest distances (that is, the �best columns) from the given observation x. Denote the three columns and their respective distances as bn1, bn2, bn3, and rn1, rn2, rn3. 3. Calculate the elements of θ as below: θ
In the preceding example, θ uses the 3 pre-measured loop configurations that are most likely the closest to the observed data's loop configuration. Moreover, θ provides weighting for calculating a loop estimation based on the 3 pre-measured loop configurations. Assuming the corresponding loop lengths for the three chosen points are 1000, 1200 and 1400 ft, respectively, the estimated loop length then will be θn1�1000+θn2�1200+θn3�1400. Bridge tap lengths can be calculated analogously.
Use of database B and interpolation weighting vector θ can be further developed into a general and mathematical formulation where a Quadratic Program (QP) technique or methodology can be used. Define r as the �residual� of the observed attenuation values that cannot be described as a linear interpolation of other database entries, where r is mathematically defined as
r = ∑ n θ n b n - x . It is clear that r is a function of the interpolation weighting vector θ. Then, the problem of finding θ can be formulated as:
min r , θ r T r such that r = B θ - x , θ ≥ 0 , ∑ θ i = 1. This mathematical formulation is known as a Quadratic Program, and various known algorithms can be used to numerically find the optimal solution r and θ for given B and x. In this mathematical QP formulation, the optimal solution θ (�optimal� in the sense of minimizing the residual error defined above) is found through a mathematical programming instead of using heuristic algorithms. Note that it may not be possible to explain x as a convex weighting of bn. Mathematically, there may not exist
θ ∈ R N : ∑ n θ n b n = x , θ n ≥ 0 , ∑ n θ n = 1. Therefore, r may be non-zero for the optimal θ that is found.
Instead of rTr, rTWr might be minimized by appropriate choice of θ, where W is a diagonal positive-definite matrix (that is, a diagonal matrix with strictly positive, diagonal elements) representing relative importance and/or reliability among the elements of r. In other words, the reliability weighting vector W (the term �reliability weighting vector� is used to distinguish the interpolation weighting vector θ) is a design parameter that can be chosen to give more weight to different line profiles used for scanning. The variable W controls the penalty in each of the components of r. Thus W may be set to give more or less priority to different elements of the residual. For example, some reported attenuation values may always be similar, regardless of the loop configuration for given modems. Their component in W could be correspondingly reduced. Stated another way, W can be used to emphasize the operational data that assists most in distinguishing (and thus identifying) different loop configurations and to de-emphasize data that is similar (and thus less distinguishing) across other loop configurations. Given a sufficient quantity of data, an adaptive algorithm can be used to control W, allowing it to be computer-controlled. W may be different, for example, for each brand and/or model of modem. That is, the weighting vector can be decided by a controller (such as a DSL optimizer and/or DSM server) after it first decides what kind of modem is on the line at the CPE, or more generally what types of modems are at both ends.
In addition to W, another matrix P can be used to introduce additional information into the optimization routine. If operational data other than attenuation values (for example, bit distribution data, etc.) indicate that the subject loop likely has a bridge tap of length between 0 and 500 ft, then it is desirable to have θn>0 for all n whose corresponding bridge tap lengths are between 0 and 500 ft. Besides, it would not be reasonable to consider loop configurations with bridge tap length of 2,000 ft when finding the best θ for the estimation. Thus, techniques can be employed to make some configurations more preferable than others. Put another way, loop configurations that are �far away� from the observations can be excluded or be made less desirable candidates by declaring a �cost� for each loop configuration. Let P denote this vector of �costs� (P is an N dimensional row vector) for each loop configuration in the database, and x denotes the observed attenuation values (and/or any other operational data used for estimating loop configurations). Note that P can be a function of x, can be a function of other operational data such as bit distribution, or can be a zero vector if no additional information is available. There is a tradeoff between minimizing ∥W1/2r∥ and minimizing the
∑ n θ n P n . This leads to the following minimization problem:
min r , θ r T Wr + P θ such that r = B θ - x , θ ≥ 0 , ∑ θ i = 1. where the variables above are r and θ, and one controls the tradeoff between minimizing ∥W1/2r∥ and minimizing the
∑ n θ n P n by scaling the weights P. The mathematical formulation above is also in the class of QP, and various known algorithms can be used to numerically find the optimal solution r and θ for given W, B, P and x. Again, note that W is the pre-designed weighting vector used to emphasize relative importance among elements of r, B is the database that is constructed from the attenuation measurements of known loop configurations using pre-determined line profiles, P is the vector of costs that is independently pre-calculated from part of operational data, and x is the observed attenuation values of pre-determined line profiles.
FIG. 10 illustrates one example when L=2. In practice, L typically is much larger than 2, however it is difficult to illustrate in a 2-dimensional figure a situation in which L>2. Moreover, extrapolation of the L=2 case to other situations with more loops will be apparent to those skilled in the art. In the example of FIG. 10, the x-axis can represent upstream attenuation for a known line-profile while the y-axis represents downstream attenuation for the same line-profile. Each loop configuration in the database is represented by each data point 1002 in the plot 1000. These data may also be developed and stored in connection with a known modem type. The DSL line to be identified and/or estimated is illustrated by point 1004, which represents the observed operational data (such as attenuation values). In this example, entries in the database that are �nearby� the observation are sought (that is, entries that do not vary substantially from the observed operational data). Ideally, the observed attenuation values would exactly equal or match one element of the loop configuration database, or be between or within a convex combination of other entries. The convex hull 1007 of 3 of the profiles 1006 (those for which θn>0) is illustrated by the dashed lines. That is, those loop configurations for which θn>0 are the extreme points of the polygon formed by the dashed lines. The estimate, shown as point 1008 within hull 1007, is
min r , θ r T Wr + P θ such that r = B θ - x , θ = e n . with the same variables as the more general case, above, and n as an additional variable. Note that en is simply a column vector of all 0's, except for the nth element which is 1. Thus, this optimization will look for the one column in B that best matches the observation x, and thus it is a restricted version of the more general case above in the sense that only θ=en is feasible.
It may also be desirable to detect �badly spliced� lines for corrective action by the operator. Such �badly spliced� lines generally can be identified because they are substantially inconsistent with entries in the loop configuration database, which is constructed in the absence of bad splices. In particular, badly spliced lines may be identified by their large, calculated residual values ∥r∥m after finding the optimal solution θ, where an arbitrary m-metric is applied (for example, 1-norm, 2-norm, ∞-norm). In some cases, the metric can weight the upstream attenuation values more heavily than downstream values because the capacitive coupling of a bad splice is most dominant in the low frequency range where upstream transmissions occur. In some embodiments of the present invention, the residual values can be compared to threshold values. When the calculated residual value exceeds the threshold, a reasonable estimation can be made that a bad splice is present on the line.
When channel insertion loss is known, another embodiment of the present invention can be utilized, as illustrated in FIG. 11. According to the method 1100 of FIG. 11, the channel identification process begins by conducting a rough bridged tap (BT) analysis considering 0 bridged taps or 1 bridged tap (a �1BT analysis�) at 1102. As part of the 1BT analysis, a rough sampling of line lengths and BT parameters is performed, yielding a set of 1BT first round (or 1BT-1) profiles. The S 1BT-1 profiles that are closest to the averaged and shifted insertion loss (where, for example, S is 20) are forwarded to a second round while the remainders are discarded. Here, the measure of closeness can be the difference metric dTOTAL, described below. For each of these S profiles, loop profiles close to the 1BT-1 profiles then are constructed with a fine sampling of line lengths and BT parameters. The aggregate of these new profiles can be called the 1BT second round (or 1BT-2) profiles. For each 1BT-2 profile, the difference metric is computed as before to find the 1BT-2 profile minimizing the metric.
loop length = 1 3 USatten _ - 2 3 for USatten _ ≥ 11 loop length = 3 11 USatten _ for USatten _ < 11 where, the result is in kilofeet and USatten is the average upstream attenuation collected for normal operational line profiles without carrier mask constraints. Otherwise, at 1114 the bridged-tap estimate is the rounded-down value of the best profile and the line length estimate the line length of the best profile.
The method 1200 of FIG. 12 takes these issues into consideration. At 1202 the averaged and bias-adjusted insertion loss reported by the modem are smoothed out by using a simple sliding-window median filter. The result is called Hsmooth. The first few tones in the downstream band of Hsmooth are ignored to avoid any analog effects from the modem, as well as to avoid windowing effects induced by the smoothing filter. As the insertion loss is generally decreasing in frequency, at 1203 the point is identified at which Hsmooth first dips below −100 dB. This cutoff point is called fcut. Tones less than fcut are the �low-variance� band and tones higher than fcut are the �high-variance� band.
MSE low = ∑ n = c f c  H ABCD , n - H smooth , n  2 ( where f c is f cut ) The c lowest tones are disregarded in this embodiment to account for modem error effects for lower tones.
verysmooth
d TOTAL=MSElow+MSEhigh Where, as in the immediately preceding example, the database methodology is not applied for channel identification, the technique described earlier for detecting bad splices through attenuation residuals is not applicable. However, bad splices can be detected based on a property of such connections�high attenuation at low frequencies due to capacitive coupling. Thus, by examining the error between the ABCD model and reported attenuation, bad splices can be detected by looking for large errors with higher than expected attenuation at low frequencies, and lower than expected attenuation at high frequencies. Furthermore, the shape of insertion loss can be used to detect the location of bad splice whenever the insertion loss is a function of bad splice's location.
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