Characterizing ingress noise

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.

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' 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' premises is strong enough to be reliably detected.

Internet and VoIP services use signals directed from the subscribers' 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' 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'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'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'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.

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 toFIG. 1, a cable network100includes a cable plant102connecting multiple customer premises104and104ato a headend106. The customer premises104and104aare connected via nodes108. The right-hand customer premises104aare expanded to show an analog TV set110connected to a TV set-top box112, a cable modem114in a wireless communication with a laptop computer115, and a VoIP phone adaptor116connected to a phone118. The TV set-top box112and the cable modem114are connected to the cable plant102via a common cable splitter120. A Cable Modem Termination System (CMTS)107is disposed at the headend106. Its function is to establish and maintain communication with the cable modems114installed in all customer premises104.

Several exemplary sources of ingress noise are shown in the customer premises104a. The ingress noise sources include an analog TV sync signal122, a power line ingress124, and a RF ingress126entering the cable through a delaminated cable shielding127. All these sources enter the cable plant102and travel towards the headend106, impeding communications with other customer premises104.

To identify the problematic customer premises104awhere the ingress noise122,124, and126is generated, a tester128is coupled at a first location131to receive a cable signal132. According to the invention, the tester128is constructed and/or programmed to determine not only spectral but also repetitious properties of ingress noise, as follows.

Referring toFIG. 2andFIGS. 3A to 3Cwith further reference toFIG. 1, a method200for characterizing ingress noise, such as the noise122,124, and126in the cable network100, includes a step201of identifying a first frequency band301(FIG. 3A) of the cable signal132at the first location131in the cable network100. In a step202, a time trace302(FIG. 3B) of the amplitude of the cable signal132in the first frequency band301is captured by the tester128. In this illustrative example, the time trace302includes a plurality of well-defined ingress noise peaks321,322,323. . . at times t1, t2, t3separated by a time interval Δt, that is, the ingress noise is periodic.

In a step203, the tester128computes an autocorrelation function303(FIG. 3C) of the time trace302. Referring specifically toFIG. 3C, the autocorrelation function303has a plurality of peaks330,331,332. . . separated by a first time delay equal to Δt. As with any autocorrelation function, the first peak330is at zero time delay. The first peak at non-zero time delay Δt is the next autocorrelation peak331. It is indicative of a repetitive component of the ingress noise, for example, the TV sync signal122, the power line ingress124, and the RF ingress126entering the cable plant102. The autocorrelation function303is 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 noise124; noise repeating at 15.73426 kHz (NTSC) or 15.625 kHz (PAL) is characteristic of the analog TV sync signal122. Thus, the measured value of Δt is indicative of a type of the ingress noise.

Referring back toFIG. 2, the tester128can report the measured value of Δt back to the headend106. Alternatively or in addition, in an optional step204, the tester128can display a frequency spectrum310(FIG. 3A) of the cable signal132, the frequency spectrum310having a first peak311in the first frequency band301due to the repetitive component of the ingress noise. In a step205(FIG. 2), the tester128can display a first repetition frequency312associated with the peak311.

The first step201of the method200can be performed by identifying, either automatically or manually, the noise peak311in the frequency spectrum310of the cable signal132at the first location131, and selecting the first frequency band301to include a central frequency of the noise peak311, as shown inFIG. 3A. The spectrum310can be measured by a spectrum analyzer module, not shown, included in the tester128as a hardware element and/or as a software/firmware function. Once the spectrum310is obtained, a user of the tester128can select specific frequencies of interest, or frequency bands of interest, within the full frequency span of the spectrum310. Alternatively, the first frequency band301, and optionally other frequency bands of interest, can be remotely provided by the headend106of the cable network100.

The time trace302can be obtained by dwelling the spectrum analyzer module at the first frequency band301for 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 spectrum310can be performed to obtain the time trace302.

In one embodiment, the first frequency band301can include an upstream digitally modulated channel, not shown. In this case, the second step202can include demodulating the cable signal132and 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 band301can be obtained in the second step202, and the autocorrelation of that time trace can be calculated in the third step203of the method200ofFIG. 2.

Thresholding can be used to eliminate non-pulsed noise and/or upstream signal bursts from the analysis. Referring toFIG. 4, a time trace402is captured when the amplitude of the cable signal132in the first frequency band301and/or its time derivative exceed a predefined threshold A1. For example, when the amplitude of a first peak421exceeds the value A1, the entire trace402is captured. If the peak amplitude were smaller, e.g. that of a second peak422, the time trace402would 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 trace402is captured only when the amplitude is within a predefined parameter range, for example between A1and A2as shown inFIG. 4. This is a useful option when the upstream bursts themselves, e.g. a third peak423, are to be excluded from captured time traces, because these bursts are typically of a high amplitude compared to noise.

Referring back toFIG. 3C, once the first autocorrelation peak331at 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 function303is sometimes so noisy that the first autocorrelation peak331at a non-zero time delay cannot be easily discerned. For this case, the autocorrelation function303can be averaged by repeatedly obtaining the time traces302e.g. 10 to 1000 times, computing the autocorrelation function303for each obtained time trace302, and then averaging the obtained autocorrelation functions303. Then, the first autocorrelation peak331can be detected more easily, and, accordingly, the time delay Δt can be found with a better precision and/or fidelity.

Still referring toFIG. 3C, the autocorrelation function303having a single periodic noise component with the time period Δt includes a plurality of peaks331,332, and other peaks, not shown, that bear information about the time interval Δt. To recover the time interval Δt from a single autocorrelation function303, which can also be averaged to improve signal-to-noise ratio, the following method can be used. Referring now toFIG. 5with further reference toFIG. 3C, a summation method500includes a step501of summing up N values of the autocorrelation function303at multiples of the time interval Δt to obtain a value S(Δt), wherein N is an integer ≧2. Then, in a step502, the previous step501is repeated at different values of the time interval Δt. Finally, in a step503, 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 signal122, the power line ingress124, and the RF ingress126described 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 function303will include at least one second autocorrelation peak, not shown, at a second non-zero time delay Δt2. The second autocorrelation peak can be much weaker than the first autocorrelation peak331. To determine the second repetition frequency even in the presence of the strong first autocorrelation peak331, the time trace302can be processed to remove the signal peaks therein corresponding to the first autocorrelation peak331, i.e. the first to third peaks321to323, respectively, and the autocorrelation function303may then be re-computed from the processed time trace303to 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 toFIG. 6A, an embodiment128A of the tester128generally includes an input terminal601for coupling to the first cable network location131, a processing unit641coupled to the input terminal601, and a display device642coupled to the processing unit641for displaying frequency spectra and noise repetition information. The processing unit641includes five distinct processing modules: an RF front end602for conditioning an input RF signal, a digitization module604for converting the input RF signal into the digital domain, a Field-Programmable Gate Array (FPGA) module606A for performing digital down-conversion, thresholding, and demodulation, a Digital Signal Processing (DSP) module608for performing autocorrelation, averaging, and repetition analysis, and a host processor module610, which is the microprocessor of the tester128, for performing function of data post-processing and preparation of the display screens. The RF front end module602includes an input protection circuit612for prevention of a burnout of sensitive gain stages and ADCs, gain stages614for amplifying the input signal, and an optional attenuation stage616. The digitization module604has the gain stages614coupled to the full-band ADC618. The gain stages614are amplifying the input signal to a level sufficient for full bit depth analog-to-digital conversion. The FPGA module606A is configured to perform the functions of digital down-conversion (DDC)620to remove the carrier frequency, triggering/thresholding622as explained above with reference toFIG. 4, and demodulation624. The DSP module608is configured to perform the functions of cyclic autocorrelation626, averaging628, and repetition analysis630as illustrated by the method500ofFIG. 5. Finally, the host processor module610is programmed to perform a function632of preparing (post-processing) the data for displaying to the user, and a function634of displaying the data on the display642of the tester128. Generally, the processing unit641can include at least one of a FPGA, a digital signal processor, and a microprocessor for performing the steps201to205of the method200ofFIG. 2.

Turning toFIG. 6B, and embodiment128B of the tester128is similar to the embodiment128A ofFIG. 6A, except that the embodiment128B ofFIG. 6Bhas an FPGA module606B configured for performing a windowing function636, a FFT function638, analysis band selection640, and triggering/thresholding622. In this configuration, the FPGA module606B is suitable for obtaining the time trace302via FFT of the frequency spectrum310, as opposed to direct measurement of the time trace302. It is to be noted, however, that obtaining the autocorrelation303of the time trace302is still performed, e.g. by the DSP function626. The autocorrelation303of the time trace302is 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 toFIG. 7with further reference toFIG. 1,FIG. 3A, andFIGS. 6A and 6B, a typical repetitious noise measurement700is performed as follows. The RF front end602of the tester128A or128B is coupled to the first location131of the cable network100. In this example, the full-band ADC618of the tester128A or128B of the digitization module604digitizes the cable signal132at 204.8 mega-samples per second, to capture the full upstream bandwidth of 85 MHz. Then, the tester128generates the spectrum310(FIG. 3A) via 2048-point real-time FFT. InFIG. 3A, only a part of the entire 85 MHz frequency range is shown for simplicity.

Once the analysis band, or the first frequency band301, is selected in a step706, the tester128A or128B proceeds to obtaining the time trace302by performing triggering708, thresholding710, (triggering/thresholding functions622of the FPGA606A or606B) and/or demodulation712(demodulation function624of FPGA606A ofFIG. 6A) of the cable signal132. Then, a cyclic autocorrelation714is performed for the time trace302(cyclic autocorrelation function626inFIGS. 6A and 6B), which in this example is 8000 measurement points long at an update rate of 25 Hz. Then, averaging716and repetition analysis718are performed as explained above. The obtained data are post-processed at720(the post-processing function632inFIGS. 6A and 6B) to display spectra annotated with repetitious noise information in a final step722(the displaying function634inFIGS. 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 toFIG. 8Awith further references toFIGS. 2 and 7, a marker800is automatically placed on the noise peak311. 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 tester128A can be configured to distinguish the sharp noise peak311from upstream communication burst peaks361. The tester128A performs measurements of repetitious noise characteristics by using the method200ofFIG. 2, with the optional details provided by the method700ofFIG. 7. The first repetition frequency is displayed at312, and the second repetition frequency is displayed at812, together with relative proportions of the noise at these frequencies into the noise peak311. The noise that does not have any repetitious components is displayed as “Irregular” at813. An alternative is shown inFIG. 8B, wherein the first frequency band301is manually selected by a technician.

Turning toFIG. 9, the repetitious properties of ingress noise are shown on a “heat map”900showing the frequency spectrum310in the color/shading form. For each frequency, the color and shading illustrate different periodicity components inside the real-time, histogram spectrum display900. Thresholding and trigger windows can be swept in level to determine and illustrate relationships902between periodicity and level.

Referring back toFIGS. 3B and 3C, when the noise peak311in the first frequency band301includes two repetitive components, the autocorrelation function303of the time trace302will include the second autocorrelation peak (not shown) at a second non-zero time delay indicative of the second repetitive component of the noise peak311. For this case, the peak311is shaded or colored, different shades or colors corresponding to the respective magnitudes of the first and second frequency components. Referring toFIG. 10A, the noise peak311includes first and second distinctly colored or shaded areas1001and1002, corresponding to the first and second repetitive noise components, respectively. The relative size and/or position of the colored or shaded areas1001and1002correspond 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 at1011and1012, 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 toFIG. 10Bwith further reference toFIG. 4, a time trace of the cable signal132in an upstream band361A is triggered at a triggering threshold1021corresponding to the lower amplitude A1inFIG. 4; in addition, the time trace302is only captured when the cable signal132is smaller than the upper threshold1022corresponding to the higher amplitude A2inFIG. 4. In this case, the shaded areas1001and1002can be plotted inside the upstream band361A, as shown inFIG. 10B.

Referring toFIG. 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 graph1110disposed under the frequency spectrum310. As a minimum, the graph1110can include at least one repetition frequency for at least one frequency band, for example, a frequency band1101showing a single repetition frequency1102of approximately 25 kHz. The repetition frequency graph can also be disposed proximate to, or superimposed with, the frequency spectrum310.

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 location131(FIG. 1) according to the method200ofFIG. 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 step203. For instance, the technician can decide to travel to the noisy customer premises104abased on previously recorded noise sources at that location.