Abstract:
Methods and devices for characterization of repetitious noise in cable networks are disclosed. A frequency band of interest is identified, a time trace of a signal parameter within the frequency band is obtained, and an autocorrelation of the time trace is computed to detect repetitious noise. The repetition frequency can serve as an indicator of the noise source type, and thus it can assist in noise segmentation.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present invention claims priority from U.S. Patent Application No. 61/703,538 filed Sep. 20, 2012, which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present invention relates to cable network maintenance, and in particular, to characterizing ingress noise in a cable network. 
     BACKGROUND OF THE INVENTION 
     A cable network delivers services such as digital television, Internet, and Voice-over-IP (VoIP) phone connection. The services are delivered over a tree-like network of a broadband coaxial cable termed a “cable plant”. Digital television signals are broadcast from a headend connected to the trunk of the cable plant, and delivered to subscribers&#39; homes connected to the branches of the cable plant. In going from the headend to the subscribers, the signals are split many times, and are attenuated in the process. Accordingly, a strong downstream broadcast signal is required, so that the signal level at the subscribers&#39; premises is strong enough to be reliably detected. 
     Internet and VoIP services use signals directed from the subscribers&#39; premises back to the headend, or “upstream” relative to the broadcast signal, which is accordingly termed “downstream” signal. The tree-like structure of the cable plant ensures that the upstream signals are brought together into the common trunk connected to the headend. Time-division multiplexing (TDM) is used to ensure that the upstream signals do not interfere with each other as they are combined. 
     Unfortunately, not only the upstream signals, but also noise can propagate in the upstream direction. The noise originates at customers&#39; premises due to improper cable grounding or shielding, non-professional equipment installation, loose connectors, unshielded indoor equipment such as electrical motors, TV sets, and the like. This ingress noise is particularly problematic in the upstream direction, because as it propagates from many end locations towards the common trunk of the cable plant, it tends to accumulate and grow in magnitude, compromising or even completely disabling digital communications, at least for some subscribers. A further problem for the upstream direction is that the upstream signals occupy a lower frequency band, typically from 5 MHz to 45 MHz, as compared to the downstream signals spanning typically from 50 MHz to 1 GHz. Thus, the upstream signals are closer in frequency to ingress noise, which tends to be a low-frequency noise. 
     The problem of the upstream ingress noise has long since been recognized. About 80% of a cable network technician&#39;s time is typically devoted to tracking down and fixing return path noise. Starting at the final common point, the technician determines which branch of the network is contributing the most noise to the network. Once a “noisy” branch is selected, the technician drives down to the next split point on that branch, and again determines the branch the noise is coming from. The technician keeps traveling down the cable plant and making measurements, until a specific network element, a shielding fault, or a home is identified as the noise source. Statistically, about 80% of radio-frequency (RF) noise has been found to have originated from a specific single customer&#39;s home. 
     Reichert in U.S. Pat. No. 4,520,508 discloses a system having a central station and a plurality of subscriber terminals specifically adapted to monitor ingress noise. Each subscriber terminal monitors certain frequencies and then provides signal level information to the headend controller. Once the headend controller has received signal level information from all of the subscriber terminals, the signal level information from all of the subscriber terminals is compared. By comparing signal levels of differently located subscriber terminals, a source of ingress may often be narrowed to a location between two of such subscriber terminals. 
     Gotwals et al. in Canadian Patent 2,308,497 disclose an improvement of the Reichert device. A impairment detection system of Gotwals et al. includes a plurality of remote units, which monitor one or more frequencies to be tested in a synchronized manner. By monitoring frequencies to be tested in a synchronized manner, intermittent leakage signals may be accurately measured and located. 
     Chappel in U.S. Pat. No. 6,425,132 discloses a method and apparatus for ingress testing a two-way cable network, which provides for remote selection of nodes to be tested and remote viewing of ingress test measurements obtained from the selected node. The “ingress modem” measures upstream spectrum and reports it to the headend. 
     Zimmerman in U.S. Pat. No. 6,978,476 discloses a device constructed to measure a local level of ingress noise at a test frequency, and to display the level of the measured noise. The device is attached at a cable junction outside of a building. A radio frequency signal at the test frequency is the radiated at the building from a test van. A technician driving the test van determines the local level of ingress by looking at the display of the device. Detrimentally, systems of Reichert, Gotwals, Chappel, and Zimmerman require custom probe installation, and thus are relatively complex. 
     Sanders et al. in U.S. Pat. Nos. 5,737,461 and 5,742,713 disclose an upstream ingress filter including a remote controllable relay that can pull the entire upstream band down (connect to ground) at a particular location, thus allowing remote segmentation of ingress noise. Detrimentally, when the upstream band at the particular location is pulled down, the normal upstream communication is disabled, disrupting the subscriber&#39;s Internet and VoIP phone services. 
     SUMMARY OF THE INVENTION 
     By performing multiple experiments and measurements of ingress noise in cable networks, the inventors have determined that the ingress noise is often repetitious in nature. It has been determined that different noise sources have different repetitious properties, in addition to different spectral properties. Accordingly, traditional methods of upstream noise characterization and/or segmentation can be enhanced by measuring and accounting for repetitious properties of the ingress noise. 
     In accordance with the invention, there is provided a method for characterizing ingress noise in a cable network, the method comprising: 
     (a) identifying a first frequency band of a cable signal at a first location in the cable network; 
     (b) obtaining a time trace of a parameter of the cable signal in the first frequency band identified in step (a); and 
     (c) computing an autocorrelation function of the time trace, wherein a first autocorrelation peak at a non-zero time delay is indicative of a repetitive component of the ingress noise. 
     From the time delay of the first autocorrelation peak, a repetition frequency of the repetitive noise component can be determined. This process can be repeated at a second location, where the noise source at the determined repetition frequency is more likely to be found. 
     The method can also include (d) displaying a frequency spectrum of the cable signal at the first cable network location, the frequency spectrum having a first peak in the first frequency band due to the first repetitive component; and (e) displaying the first repetition frequency of the first repetitive component. 
     In accordance with the invention, there is further provided a device for carrying out the above method, the device comprising an input terminal for coupling to the first cable network location, a processing unit coupled to the input terminal and configured for performing at least steps (b) and (c) above, and a display coupled to the processing unit and configured for performing steps (d) and (e), e.g. displaying the spectral peaks and their repetitious properties. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Exemplary embodiments will now be described in conjunction with the drawings, in which: 
         FIG. 1  is a block diagram of a cable network, showing ingress noise sources and a tester of the invention; 
         FIG. 2  is a flow chart of a method for characterizing ingress noise according to the invention; 
         FIG. 3A  is a spectral plot showing a noise frequency band to illustrate the method of  FIG. 2 ; 
         FIG. 3B  is a time trace of a signal amplitude in the noise frequency band of  FIG. 3A ; 
         FIG. 3C  is an autocorrelation function of the time trace of  FIG. 3B ; 
         FIG. 4  is a time trace of a noise signal showing a threshold and a predefined parameters range; 
         FIG. 5  is a flow chart of an autocorrelation summation analysis according to the invention; 
         FIGS. 6A and 6B  are block diagram of two embodiments of a tester of the invention for practicing the method illustrated in  FIG. 2 ; 
         FIG. 7  is a flow chart of a typical ingress noise characterization process showing some elements of the tester of  FIGS. 6A / 6 B; 
         FIGS. 8A and 8B  are exemplary information displays of the tester of  FIGS. 1, 6A, and 6B , showing repetitious properties of an ingress noise band at 4 MHz; 
         FIG. 9  is an exemplary information display of the tester of  FIGS. 1, 6A, and 6B , showing repetitious properties of multiple ingress noise bands; 
         FIGS. 10A and 10B  are exemplary information displays of the tester of  FIGS. 1, 6A, and 6B , showing repetitious properties of an out-of-band and in-band ingress noise, respectively, by means of differently shaded or colored areas; and 
         FIG. 11  is an exemplary information display of the tester of  FIGS. 1, 6A, and 6B , wherein the repetition frequencies of the frequency peaks are displayed on a common frequency axis graph disposed under the frequency spectrum. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     While the present teachings are described in conjunction with various embodiments and examples, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives and equivalents, as will be appreciated by those of skill in the art. 
     Referring to  FIG. 1 , a cable network  100  includes a cable plant  102  connecting multiple customer premises  104  and  104   a  to a headend  106 . The customer premises  104  and  104   a  are connected via nodes  108 . The right-hand customer premises  104   a  are expanded to show an analog TV set  110  connected to a TV set-top box  112 , a cable modem  114  in a wireless communication with a laptop computer  115 , and a VoIP phone adaptor  116  connected to a phone  118 . The TV set-top box  112  and the cable modem  114  are connected to the cable plant  102  via a common cable splitter  120 . A Cable Modem Termination System (CMTS)  107  is disposed at the headend  106 . Its function is to establish and maintain communication with the cable modems  114  installed in all customer premises  104 . 
     Several exemplary sources of ingress noise are shown in the customer premises  104   a . The ingress noise sources include an analog TV sync signal  122 , a power line ingress  124 , and a RF ingress  126  entering the cable through a delaminated cable shielding  127 . All these sources enter the cable plant  102  and travel towards the headend  106 , impeding communications with other customer premises  104 . 
     To identify the problematic customer premises  104   a  where the ingress noise  122 ,  124 , and  126  is generated, a tester  128  is coupled at a first location  131  to receive a cable signal  132 . According to the invention, the tester  128  is constructed and/or programmed to determine not only spectral but also repetitious properties of ingress noise, as follows. 
     Referring to  FIG. 2  and  FIGS. 3A to 3C  with further reference to  FIG. 1 , a method  200  for characterizing ingress noise, such as the noise  122 ,  124 , and  126  in the cable network  100 , includes a step  201  of identifying a first frequency band  301  ( FIG. 3A ) of the cable signal  132  at the first location  131  in the cable network  100 . In a step  202 , a time trace  302  ( FIG. 3B ) of the amplitude of the cable signal  132  in the first frequency band  301  is captured by the tester  128 . In this illustrative example, the time trace  302  includes a plurality of well-defined ingress noise peaks  321 ,  322 ,  323  . . . at times t 1 , t 2 , t 3  separated by a time interval Δt, that is, the ingress noise is periodic. 
     In a step  203 , the tester  128  computes an autocorrelation function  303  ( FIG. 3C ) of the time trace  302 . Referring specifically to  FIG. 3C , the autocorrelation function  303  has a plurality of peaks  330 ,  331 ,  332  . . . separated by a first time delay equal to Δt. As with any autocorrelation function, the first peak  330  is at zero time delay. The first peak at non-zero time delay Δt is the next autocorrelation peak  331 . It is indicative of a repetitive component of the ingress noise, for example, the TV sync signal  122 , the power line ingress  124 , and the RF ingress  126  entering the cable plant  102 . The autocorrelation function  303  is preferably a cyclic autocorrelation. 
     Each type of ingress noise has its own characteristic repetition rate. For example, noise repeating at submultiples of 16.67 ms (US) or 20 ms (Europe) is characteristic of the power line ingress noise  124 ; noise repeating at 15.73426 kHz (NTSC) or 15.625 kHz (PAL) is characteristic of the analog TV sync signal  122 . Thus, the measured value of Δt is indicative of a type of the ingress noise. 
     Referring back to  FIG. 2 , the tester  128  can report the measured value of Δt back to the headend  106 . Alternatively or in addition, in an optional step  204 , the tester  128  can display a frequency spectrum  310  ( FIG. 3A ) of the cable signal  132 , the frequency spectrum  310  having a first peak  311  in the first frequency band  301  due to the repetitive component of the ingress noise. In a step  205  ( FIG. 2 ), the tester  128  can display a first repetition frequency  312  associated with the peak  311 . 
     The first step  201  of the method  200  can be performed by identifying, either automatically or manually, the noise peak  311  in the frequency spectrum  310  of the cable signal  132  at the first location  131 , and selecting the first frequency band  301  to include a central frequency of the noise peak  311 , as shown in  FIG. 3A . The spectrum  310  can be measured by a spectrum analyzer module, not shown, included in the tester  128  as a hardware element and/or as a software/firmware function. Once the spectrum  310  is obtained, a user of the tester  128  can select specific frequencies of interest, or frequency bands of interest, within the full frequency span of the spectrum  310 . Alternatively, the first frequency band  301 , and optionally other frequency bands of interest, can be remotely provided by the headend  106  of the cable network  100 . 
     The time trace  302  can be obtained by dwelling the spectrum analyzer module at the first frequency band  301  for a period of time, and capturing an output signal of the spectrum analyzer module. Alternatively, a real-time fast Fourier transform (FFT) of the obtained spectrum  310  can be performed to obtain the time trace  302 . 
     In one embodiment, the first frequency band  301  can include an upstream digitally modulated channel, not shown. In this case, the second step  202  can include demodulating the cable signal  132  and obtaining a symbol error vector of the demodulation. A time trace of the symbol error vector is then constructed and processed in a same manner as the signal amplitude, that is, an autocorrelation function can be computed, and peaks of that autocorrelation function can be detected. A time trace of the error vector or any other parameter of the signal in the first frequency band  301  can be obtained in the second step  202 , and the autocorrelation of that time trace can be calculated in the third step  203  of the method  200  of  FIG. 2 . 
     Thresholding can be used to eliminate non-pulsed noise and/or upstream signal bursts from the analysis. Referring to  FIG. 4 , a time trace  402  is captured when the amplitude of the cable signal  132  in the first frequency band  301  and/or its time derivative exceed a predefined threshold A 1 . For example, when the amplitude of a first peak  421  exceeds the value A 1 , the entire trace  402  is captured. If the peak amplitude were smaller, e.g. that of a second peak  422 , the time trace  402  would not be collected. Another parameter such as the error vector mentioned above can be used in place of the mere signal amplitude. 
     In one embodiment, the time trace  402  is captured only when the amplitude is within a predefined parameter range, for example between A 1  and A 2  as shown in  FIG. 4 . This is a useful option when the upstream bursts themselves, e.g. a third peak  423 , are to be excluded from captured time traces, because these bursts are typically of a high amplitude compared to noise. 
     Referring back to  FIG. 3C , once the first autocorrelation peak  331  at one non-zero time delay Δt is detected, the first repetition frequency f of the ingress noise can be determined from the value of Δt as f=1/Δt. However, the autocorrelation function  303  is sometimes so noisy that the first autocorrelation peak  331  at a non-zero time delay cannot be easily discerned. For this case, the autocorrelation function  303  can be averaged by repeatedly obtaining the time traces  302  e.g. 10 to 1000 times, computing the autocorrelation function  303  for each obtained time trace  302 , and then averaging the obtained autocorrelation functions  303 . Then, the first autocorrelation peak  331  can be detected more easily, and, accordingly, the time delay Δt can be found with a better precision and/or fidelity. 
     Still referring to  FIG. 3C , the autocorrelation function  303  having a single periodic noise component with the time period Δt includes a plurality of peaks  331 ,  332 , and other peaks, not shown, that bear information about the time interval Δt. To recover the time interval Δt from a single autocorrelation function  303 , which can also be averaged to improve signal-to-noise ratio, the following method can be used. Referring now to  FIG. 5  with further reference to  FIG. 3C , a summation method  500  includes a step  501  of summing up N values of the autocorrelation function  303  at multiples of the time interval Δt to obtain a value S(Δt), wherein N is an integer ≧2. Then, in a step  502 , the previous step  501  is repeated at different values of the time interval Δt. Finally, in a step  503 , a value of the time interval Δt is selected that corresponds to a maximum value of S(Δt). To save computational resources, the time interval Δt can be selected to correspond to periods of known types of periodic interference, such as the analog TV sync signal  122 , the power line ingress  124 , and the RF ingress  126  described above. Alternatively, the entire curve S(Δt) can be calculated, for a range of values of Δt. 
     The ingress noise can include components at two or more repetition frequencies. In this case, the autocorrelation function  303  will include at least one second autocorrelation peak, not shown, at a second non-zero time delay Δt 2 . The second autocorrelation peak can be much weaker than the first autocorrelation peak  331 . To determine the second repetition frequency even in the presence of the strong first autocorrelation peak  331 , the time trace  302  can be processed to remove the signal peaks therein corresponding to the first autocorrelation peak  331 , i.e. the first to third peaks  321  to  323 , respectively, and the autocorrelation function  303  may then be re-computed from the processed time trace  303  to find the second autocorrelation peak. The signal peaks can be removed by identifying peaks at the first time delay Δt, removing the data points corresponding to the peaks, and using linear or polynomial interpolation to fill in the removed data points. 
     Referring to  FIG. 6A , an embodiment  128 A of the tester  128  generally includes an input terminal  601  for coupling to the first cable network location  131 , a processing unit  641  coupled to the input terminal  601 , and a display device  642  coupled to the processing unit  641  for displaying frequency spectra and noise repetition information. The processing unit  641  includes five distinct processing modules: an RF front end  602  for conditioning an input RF signal, a digitization module  604  for converting the input RF signal into the digital domain, a Field-Programmable Gate Array (FPGA) module  606 A for performing digital down-conversion, thresholding, and demodulation, a Digital Signal Processing (DSP) module  608  for performing autocorrelation, averaging, and repetition analysis, and a host processor module  610 , which is the microprocessor of the tester  128 , for performing function of data post-processing and preparation of the display screens. The RF front end module  602  includes an input protection circuit  612  for prevention of a burnout of sensitive gain stages and ADCs, gain stages  614  for amplifying the input signal, and an optional attenuation stage  616 . The digitization module  604  has the gain stages  614  coupled to the full-band ADC  618 . The gain stages  614  are amplifying the input signal to a level sufficient for full bit depth analog-to-digital conversion. The FPGA module  606 A is configured to perform the functions of digital down-conversion (DDC)  620  to remove the carrier frequency, triggering/thresholding  622  as explained above with reference to  FIG. 4 , and demodulation  624 . The DSP module  608  is configured to perform the functions of cyclic autocorrelation  626 , averaging  628 , and repetition analysis  630  as illustrated by the method  500  of  FIG. 5 . Finally, the host processor module  610  is programmed to perform a function  632  of preparing (post-processing) the data for displaying to the user, and a function  634  of displaying the data on the display  642  of the tester  128 . Generally, the processing unit  641  can include at least one of a FPGA, a digital signal processor, and a microprocessor for performing the steps  201  to  205  of the method  200  of  FIG. 2 . 
     Turning to  FIG. 6B , and embodiment  128 B of the tester  128  is similar to the embodiment  128 A of  FIG. 6A , except that the embodiment  128 B of  FIG. 6B  has an FPGA module  606 B configured for performing a windowing function  636 , a FFT function  638 , analysis band selection  640 , and triggering/thresholding  622 . In this configuration, the FPGA module  606 B is suitable for obtaining the time trace  302  via FFT of the frequency spectrum  310 , as opposed to direct measurement of the time trace  302 . It is to be noted, however, that obtaining the autocorrelation  303  of the time trace  302  is still performed, e.g. by the DSP function  626 . The autocorrelation  303  of the time trace  302  is preferred over FFT of time trace, because ingress noise is typically pulsed in nature. A FFT of a repetitious pulse has the pulse spectrum superimposed with the repetition frequency spectrum, which makes the FFT spectrum so rich in features that a repetition analysis is more difficult with FFT than with autocorrelation analysis. 
     Referring now to  FIG. 7  with further reference to  FIG. 1 ,  FIG. 3A , and  FIGS. 6A and 6B , a typical repetitious noise measurement  700  is performed as follows. The RF front end  602  of the tester  128 A or  128 B is coupled to the first location  131  of the cable network  100 . In this example, the full-band ADC  618  of the tester  128 A or  128 B of the digitization module  604  digitizes the cable signal  132  at 204.8 mega-samples per second, to capture the full upstream bandwidth of 85 MHz. Then, the tester  128  generates the spectrum  310  ( FIG. 3A ) via 2048-point real-time FFT. In  FIG. 3A , only a part of the entire 85 MHz frequency range is shown for simplicity. 
     Once the analysis band, or the first frequency band  301 , is selected in a step  706 , the tester  128 A or  128 B proceeds to obtaining the time trace  302  by performing triggering  708 , thresholding  710 , (triggering/thresholding functions  622  of the FPGA  606 A or  606 B) and/or demodulation  712  (demodulation function  624  of FPGA  606 A of  FIG. 6A ) of the cable signal  132 . Then, a cyclic autocorrelation  714  is performed for the time trace  302  (cyclic autocorrelation function  626  in  FIGS. 6A and 6B ), which in this example is 8000 measurement points long at an update rate of 25 Hz. Then, averaging  716  and repetition analysis  718  are performed as explained above. The obtained data are post-processed at  720  (the post-processing function  632  in  FIGS. 6A and 6B ) to display spectra annotated with repetitious noise information in a final step  722  (the displaying function  634  in  FIGS. 6A and 6B ). The time trace can be between 500 to 24,000 points long, and the update rate is preferably higher than 8 Hz. 
     Examples of processing results of repetitious upstream noise will now be given. Referring to  FIG. 8A  with further references to  FIGS. 2 and 7 , a marker  800  is automatically placed on the noise peak  311 . Any suitable peak detection method can be used. For example, an absolute peak can be found using a gradient method, or a 3 dB bandwidth center can be calculated for each peak. Alternatively, the marker can be placed manually by the user. The tester  128 A can be configured to distinguish the sharp noise peak  311  from upstream communication burst peaks  361 . The tester  128 A performs measurements of repetitious noise characteristics by using the method  200  of  FIG. 2 , with the optional details provided by the method  700  of  FIG. 7 . The first repetition frequency is displayed at  312 , and the second repetition frequency is displayed at  812 , together with relative proportions of the noise at these frequencies into the noise peak  311 . The noise that does not have any repetitious components is displayed as “Irregular” at  813 . An alternative is shown in  FIG. 8B , wherein the first frequency band  301  is manually selected by a technician. 
     Turning to  FIG. 9 , the repetitious properties of ingress noise are shown on a “heat map”  900  showing the frequency spectrum  310  in the color/shading form. For each frequency, the color and shading illustrate different periodicity components inside the real-time, histogram spectrum display  900 . Thresholding and trigger windows can be swept in level to determine and illustrate relationships  902  between periodicity and level. 
     Referring back to  FIGS. 3B and 3C , when the noise peak  311  in the first frequency band  301  includes two repetitive components, the autocorrelation function  303  of the time trace  302  will include the second autocorrelation peak (not shown) at a second non-zero time delay indicative of the second repetitive component of the noise peak  311 . For this case, the peak  311  is shaded or colored, different shades or colors corresponding to the respective magnitudes of the first and second frequency components. Referring to  FIG. 10A , the noise peak  311  includes first and second distinctly colored or shaded areas  1001  and  1002 , corresponding to the first and second repetitive noise components, respectively. The relative size and/or position of the colored or shaded areas  1001  and  1002  correspond to a relative magnitude of the first and second repetitive noise components. In this way, the first and second repetition frequencies can be color-coded by their representative colors, as indicated at  1011  and  1012 , respectively. 
     It is to be noted that not only pure noise peaks, but also noise within communication spectral bands can be displayed in this manner. Turning to  FIG. 10B  with further reference to  FIG. 4 , a time trace of the cable signal  132  in an upstream band  361 A is triggered at a triggering threshold  1021  corresponding to the lower amplitude A 1  in  FIG. 4 ; in addition, the time trace  302  is only captured when the cable signal  132  is smaller than the upper threshold  1022  corresponding to the higher amplitude A 2  in  FIG. 4 . In this case, the shaded areas  1001  and  1002  can be plotted inside the upstream band  361 A, as shown in  FIG. 10B . 
     Referring to  FIG. 11 , a similar embodiment of presenting repetition properties of ingress noise is presented. A plurality of repetition frequencies for each frequency are displayed on a common frequency axis graph  1110  disposed under the frequency spectrum  310 . As a minimum, the graph  1110  can include at least one repetition frequency for at least one frequency band, for example, a frequency band  1101  showing a single repetition frequency  1102  of approximately 25 kHz. The repetition frequency graph can also be disposed proximate to, or superimposed with, the frequency spectrum  310 . 
     While a detailed set of repetition frequencies is usually not known for each network location, fair assumptions can often be made as what type of noise may be prevalent in what network area. As technicians learn new sources of ingress noise, they can associate those sources with particular network locations for future use. By way of a non-limiting example, the ingress noise can be characterized at the first location  131  ( FIG. 1 ) according to the method  200  of  FIG. 2 , and then the network technician can proceed to a second cable network location associated with the first repetition frequency, determined upon computing the autocorrelation function in step  203 . For instance, the technician can decide to travel to the noisy customer premises  104   a  based on previously recorded noise sources at that location. 
     The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function. 
     The foregoing description of one or more embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto.