Abstract:
A prequalification determination is made on a DSL line to predict the bit rate improvement that might be accomplished by implementation of vectoring, providing information to use in determining whether or not to implement vectoring. Overall noise is measured on the line, along with FEXT noise, and the FEXT noise is subtracted from the overall noise, and the resulting value is employed to determine a predicted bit rate, providing an indication of the potential bit rate if vectoring is implemented.

Description:
BACKGROUND 
     This disclosure relates to prequalification of Digital Subscriber Line (DSL), and more particularly to estimating the effect of implementing vectoring at a prequalification stage. 
     DSL is a technology that transmits data over a local telephone network and prequalification is a procedure to detect conditions and estimate performance of data transmission, such as bitrate, on the line. 
     The performance of the DSL is affected by noise such as Near-End Crosstalk (NEXT), Far-End Crosstalk (FEXT), impulse and white noise. 
     Vectoring is a transmission method that employs the coordination of line signals to provide cancelling and reducing of FEXT levels and improvement of performance. To implement vectoring and conditioning of the signal, extra devices need to be implemented in the Access Node (AN) located at a Central Office (CO). Vectoring requires implementation of a heavy signal processing to cancel FEXT noise. 
     Though vectoring cancels FEXT and greatly improves rate and reach performance, vectoring cannot achieve full improvement when there are non-vectored lines or lines form separate vector group. 
     SUMMARY 
     In accordance with the disclosure, a prequalification determination is made on a DSL line to predict the bit rate improvement that might be accomplished by implementation of vectoring, thus assisting in deciding whether or not to implement vectoring in an individual situation. 
     Accordingly, it is an advantage of the present disclosure to provide an improved method and apparatus for predicting bit rate improvement that might be had by implementing vectoring. 
     It is a further advantage of the present disclosure to provide an improved tool to help with a decision to implement vectoring. 
     Since vectoring cannot perform fully with the co-existence of non-vectored lines, it is a further advantage of the disclosure to employ a reference model that includes the effect of co-existence of non-vectored lines and gives accurate prediction of DSL performance by the expected numbers of vectoring and non-vectoring lines. 
     The subject matter of the present technology is particularly pointed out and distinctly claimed in the concluding portion of this specification. However, both the organization and method of operation, together with further advantages and embodiments thereof, may best be understood by reference to the following description taken in connection with accompanying drawings wherein like reference characters refer to like elements. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1 and 2  are block diagrams of a network model for vectored system with instrument for prequalification; 
         FIG. 3  is a flow chart of the operational steps; and 
         FIG. 4  is a fuzzy neural networks structure. 
     
    
    
     DETAILED DESCRIPTION 
     The system according to a preferred embodiment of the present disclosure comprises measurement instrument capable of measuring network performance including determining overall noise, and determining FEXT noise; and a system that can predict the effect of FEXT noise cancellation and calculate bit rates with vectoring (FEXT noise cancelled) and without vectoring. 
     Referring to  FIG. 1 , a block diagram of a network model for vectored system with instrument for prequalification, to implement DSL, Digital Subscriber Line Access Multiplexer (DSLAM)  12  needs to be implemented at a Central Office (CO) site,  14 . Cables  16  contain copper line(s)  18  to each Remote Terminal (RT)  20  that contains a DSL Modem  22 . Test instrument  24  comprises components  24   a  and  24   b  located at both CO and RT sites to measure both NEXT noise at CO site and FEXT noise at RT site of the subscribed line. The test instrument may be connected to PC  26  to upload and download, store, and manipulate data. 
     Referring to  FIG. 2 , alternatively, the test instrument  24   c  can be located at only CO site  14  to measure NEXT only. In this case, the FEXT noise can be estimated using the NEXT noise measurement. 
     Referring to  FIG. 3 , the operational steps performed by the instrument are described, wherein operation begins with a first step  28 , wherein NEXT noise at CO site  14  and optionally FEXT noise at RT site  20  are measured. The measurement instrument not only measures noise but also performs metallic testing including measurement of attenuation, wideband longitudinal balance and bridge tap detection to measure metallic conditions that will affect to the DSL performance. The measured data is stored in storage  30 , located in either in the measurement instrument  24  or in PC  26  (via an external link). FEXT noise can be measured either at RT site  20  by use of Dual End Line Test (DELT) in step  28  as in  FIG. 1  or can be estimated in step  32  as in  FIG. 2  from the NEXT noise measurement at CO site  14  by Single End Line Test (SELT). FEXT noise is estimated with attenuation, wide band longitudinal and the NEXT noise measurement data. The instrument  24  calculates precise FEXT noise with the NEXT noise and metallic conditions (length and wideband longitudinal balance data). 
     Next, step  34 , as one of the performance matrix of DSL, bitrate is calculated with attenuation, wide band longitudinal balance, and noise information. The noise information included is both the NEXT and FEXT noise measurement (or estimated FEXT noise). 
     Then, step  36 , bitrate is calculated with the FEXT noise cancellation from the FEXT Cancellation Model  38  that emulates the FEXT cancellation (vectoring). The model  38  will give cancelled FEXT noise, since the actual cancellation accomplished is likely to not cancel FEXT noise entirely, so a model of the expected cancellation is provided. The estimated cancelled FEXT noise will be used instead of the measured FEXT noise to estimate the effect of cancellation. The model can be constructed, for example, as described in the publication  Near - End and Far - End Crosstalk Statistical Models , COMMITTEE T1-TELECOMMUNICATIONS T1E1.4/99-527, Working Group T1E1.4 (DSL Access), Clearwater, Fla., Dec. 6-10, 1999, employing Artificial Intelligent model instead of the mathematical model in the publication. Polynomial Fuzzy Neural Network (PFNN) is used to model vectoring scheme and Genetic Algorithms (GA) is used to optimize the model. PFNN is method to design data-driven nonlinear model. The model  38  is designed offline with noise (NEXT and FEXT), and metallic data (length and wideband longitudinal data) and binding information such as number of pairs in binding group, number of vectored lines, and co-existence of SDSL. 
     The input of the model  38  is NEXT noise, (estimated) FEXT noise, attenuation (by length), wideband longitudinal balance, actual FEXT cancellation by vectoring device, and number of pairs in binding group, number of vectored lines, and number of SDSL lines. The model gives estimated FEXT noise cancellation. Once the model  38  is designed, it gives FEXT Cancellation (vectoring) online with the noise and metallic data measured in steps  28 ,  32  and  34 . 
     And finally, the benefit of FEXT noise cancellation, or vectoring, is evaluated and reported in step  40 . The difference of bitrate with FEXT cancellation model and without the FEXT cancellation model is the benefit of the implementation of the vectoring. 
     Mathematical Equations of FEXT 
     In step  34 , FEXT noise can either measured at RT side  20  with instrument  24   b  or can be estimated. FEXT can be estimated as an attenuated value of NEXT by length of cable. Thus, FEXT can be represented as a function of NEXT, frequency, length of cable and number of pairs in a binder. FEXT can be represented using a mathematical equation of frequency f, length d, and number of pairs in a binder n as equation (1). We use a polynomial fuzzy neural network to model FEXT accurately. FEXT is estimated with insertion loss IL, NEXT, length d, and number of pairs in a binder n as equation (2).
 
FEXT( f )=3 10 −4 ( n/ 49) 0.6   f   2   d   (1)
 
FEXT( f )= f ( IL ( f,d ),NEXT( f,n ), d,n )  (2)
 
     Where, insertion loss IL(f,d) can be represented in mathematical equation as in (3). Here, we use a measured value by test instrument  24   c  in  FIG. 2 .
 
 IL ( f )=exp(−2 kdf   1/2 )  (3)
 
Polynomial Fuzzy Neural Network Optimized with Genetic Algorithm
 
     The FEXT model in step  34  and the FEXT cancellation model in step  38  are implemented using a polynomial fuzzy neural network (PFNN). Each membership function of the fuzzy neural network is optimized with a genetic algorithm (GA). With PFNN, a nonlinear model can be driven through input-output data. GA is very effective on searching (optimizing) when function spaces are not smooth or continuous, which is the general case of nonlinear modeling. 
     Let&#39;s assume that the reference model can be represented in Ivahnenko polynomial [1] as in equation (4).
 
{circumflex over ( y )}= f ( x   1   ,x   2   , . . . ,x   N )= w   0   +Σw   1   x   1   +Σw   1   x   1   2   +Σw   1   x   1   x   2   + . . . +Σw   1   x   N   2   (4)
 
[1] A. G. Ivahnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man Cybern., pp. 364-378, SMC-1, 1971.
 
     The equation can be represented as the fuzzy neural networks structure as in  FIG. 4 . 
     The PFNN is a feed-forward network that computes a polynomial function of a set of parallel inputs to generate an output. 
     The premise part in  FIG. 4  is implemented by fuzzy neural networks. The inference system is implemented based on the Takagi, Sugeno, and Kang fuzzy model [2]. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select rules. 
     [2] M. Sugeno and G. T. Kang, “Structure identification of fuzzy model”, Fuzzy Sets and Systems, vol. 28, pp. 15-33, 1988. 
     The input nodes (neurons) are implemented with Gaussian fuzzy membership function given by in equation (5).
 
μ Ai ( x )=exp(−( c   i   −x ) 2 /(2σ i   2 ))  (5)
 
where, c i , and σ i  are the center and width of the i-th fuzzy set A i , respectively.
 
     The parameters (fuzzy membership functions and weight of each node) are optimized using genetic algorithm (GA) with the mean square error criteria to minimize the difference between reference model output ŷ and the actual output y. 
     The pseudo code of genetic algorithm is described below. 
     
       
         
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 Procedure Genetic_Algorithm 
               
               
                   
                 { 
               
               
                   
                   initialize_population 
               
               
                   
                   evaluate_population 
               
               
                   
                   while ( not termination_condition ) do 
               
               
                   
                   { 
               
               
                   
                     select_parents_for_reproduction 
               
               
                   
                     crossover 
               
               
                   
                     mutation 
               
               
                   
                     evaluate_population 
               
               
                   
                   } 
               
               
                   
                 } 
               
               
                   
                   
               
             
          
         
       
     
     The detail descriptions and algorithms of PFNN and GA can be found in the below references. 
     [3] Wonoh Kim, “Hierarchical Control System with Statistical Process Control and Soft Computing”, Ph.D Dissertation Proposal, School of Electrical and Computer Engineering, Georgia Institute of Technology, 2000. 
     [4] Wonoh Kim and George Vachtsevanos, “Hierarchical Process Control by Combining SPC and Soft Computing Methods”, 19th Int. Conf. of the North American Fuzzy Information Processing Society (NAFIPS 2000), pp. 485-489, 2000. 
     [5] Wonoh Kim et al., “Fuzzy Logic Ratio Control for a CVT Hydraulic Module”, Proc. IEEE Int. Symp. on Intelligent Control, pp. 151-156, 2000. 
     [6] Wonoh Kim et al., “Autonomous Vehicles, From Flight Control to Mission Planning using Fuzzy Logic Techniques”, 13th Int. Conf. on Digital Signal Processing Proceedings (DSP 97), vol. 2, pp. 977-981, 1997. 
     FEXT Cancellation Model 
     Vectoring can greatly improve the performance of DSL by removing the FEXT within a vectored group (self-FEXT). Yet in the reality there may be simultaneous presence of vectored and non-vectored lines and also multiple vectoring groups as existing DSL equipment is replaced with vectored DSL. Crosstalk from non-vectored lines or from lines belonging to other vectored groups within the same cable or binder (alien-FEXT) may not always be removed within a vectored group and it will degrade vectoring performance. 
     The suggested FEXT cancellation model in step  38  of  FIG. 3  is designed with the information of which pairs will be implemented with vectoring. The model also includes existence of special pairs of HDSL (or T1), which also frequently happens in crossbox. 
     The FEXT cancellation model is designed with PFNN described above. The PFNN model is a data driven model, not a mathematical model and can be easily model complicated system. The model includes not only noise information (NEXT, FEXT) and metallic information (attenuation and wideband longitudinal balance) but also information of number of pairs in binding group n, number of vectored lines m (the number of non-vectored lines will be n-m), co-existence of SDSL (Symmetric DSL including T1 and HDSL) p, and band plan (profiles) to get accurate estimation of the vectoring effect. 
     Accordingly a method of designing reference model of vectoring effect is provided, using artificial intelligence method such as fuzzy logic, neural networks, and genetic algorithm, but not just limited on method using artificial intelligence but any realization of the data-driven model to calculate performance of vectoring. 
     Co-existence of vectored and non-vectored pairs in the binding group and co-existence of special lines such as Symmetric DSL including HDSL and T1 lines is also accommodated. 
     In accordance with the disclosure, a determination can be made as to what improvement in data rates might be achieved with implementation of vectoring. 
     While a preferred embodiment of the technology has been shown and described, it will be apparent to those skilled in the art that many changes and modifications may be made without departing from the broader aspects. The appended claims are therefore intended to cover all such changes and modifications as fall within the true spirit and scope of the technology.