INGRESS NOISE REDUCTION IN A DIGITAL RECEIVER

• US 20090003372A1
• Filed: 04/21/2008
• Published: 01/01/2009
• Est. Priority Date: 04/12/2001
• Status: Active Grant

• Current Assignee: Juniper Networks Incorporated
• Original Assignee: Juniper Networks Incorporated
• Inventors: Buda, Fabien, Sari, Hikmet, Popper, Ambroise
• Granted Patent: US 7,826,569 B2
• Time in Patent Office: Days
• US Class Current: 370/442
• CPC Class Codes: G11B 20/22 for reducing distortions
• CPC Class Codes: H04B 1/123 using adaptive balancing or compensation means adaptive filter circuits and algorithms H03H
• CPC Class Codes: H04N 7/102 Circuits therefor, e.g. noise reducers, equalisers, amplifiers H04N7/108 takes precedence

Abstract

The invention provides a device for reducing ingress noise in a digital signal, comprising a noise predictor for predicting an amount of ingress noise in the digital signal based on past samples of the ingress noise, and a subtractor for subtracting the predicted amount of ingress noise from the digital signal. Channel distortion is compensated for by a noise-independent equalizer, such as a ZF equalizer, placed upstream of the noise predictor. The device may be incorporated, for example, in a cable modem termination system (CMTS) of an hybrid fiber/coax (HFC) network.

Claims

1-25. -25. (canceled)

26. A device, comprising:

a noise predictor circuit configured to determine an amount of predicted noise (û

_n) at a time cycle n in a sample (s_n) of digital data using predictor coefficients, where the sample (s_n) comprises useful data (x_n) and an actual noise sample (u_n); and

an adaptation circuit configured to determine the predictor coefficients of the noise predictor circuit for a future time cycle n+1 based on an adaptation step size, the predicted noise (û

_n) at the time cycle n, actual noise samples at time cycles n, n−

1, n−

2, . . . n−

M, and predictor coefficients from time cycle n, where the adaptation circuit is further configured to determine the predictor coefficients of the noise predictor circuit based on a multiplication of the adaptation step size with a value associated with a difference between the actual noise sample (u_n) at time sample n and the predicted noise (û

_n).

27. The device of claim 26, further comprising:

a first subtractor configured to subtract the amount of predicted noise (û

_n) from the sample (s_n) of the digital data to produce subtracted data (s_n−

û

_n);

a decision circuit configured to compare the subtracted data (s_n−

û

_n) with a set of thresholds to produce a decided symbol (d_n) representative of the useful data (x_n) in the sample (s_n); and

a second subtractor configured to subtract the decided symbol (d_n) from the sample (s_n) to produce the actual noise sample (u_n).

28. The device of claim 26, where the adaptation circuit is configured to determine the predictor coefficients (c_kⁿ⁺¹) of the noise predictor circuit for a future time cycle n+1 according to:

$\left(\begin{array}{c}{c}_1^{n+1}\\ c_2^{n+1}\\ \mathrm{⋮<br><br>}\\ c_M^{n+1}\end{array}\right)=\left(\begin{array}{c}{c}_1^n\\ c_2^n\\ \mathrm{⋮<br><br>}\\ c_M^n\end{array}\right)+\mathrm{\Delta <br><br>}\times <br><br>{\left(u_n-{\hat{u}}_n\right)}^{*}<br><br>\left(\begin{array}{c}{u}_{n-1}\\ u_{n-2}\\ \mathrm{⋮<br><br>}\\ u_{n-M}\end{array}\right)$

where Δ

is the adaptation step size,

u_n, u<sub>n−

1</sub>, . . . , u<sub>n−

M</sub> are the actual noise samples at time cycles n, n−

1, . . . , n−

M, and

û

_n s the amount of predicted noise at time cycle n.

29. The device of claim 28, where the noise predictor determines the amount of predicted noise û

_n according to;

${\hat{u}}_n=\underset{k=1}{\overset{M}{\sum <br><br>}}<br><br>c_k^{n^{*}}<br><br>u_{n-k}$

where c_kⁿ are the predictor coefficients of the noise predictor circuit at the time cycle n.

30. The device of claim 26, where the adaptation circuit is further configured to determine the predictor coefficients of the noise predictor circuit for the future time cycle n+1 using a tap leakage technique.

31. The device of claim 30, where the adaptation circuit is configured to determine the predictor coefficients (c_kⁿ⁺¹) of the noise predictor circuit for the future time cycle n+1 according to:

$\left(\begin{array}{c}{c}_1^{n+1}\\ c_2^{n+1}\\ \mathrm{⋮<br><br>}\\ c_M^{n+1}\end{array}\right)=\left(1-\mathrm{\mu <br><br>}\times <br><br>\mathrm{\Delta <br><br>}\right)<br><br>\left(\begin{array}{c}{c}_1^n\\ c_2^n\\ \mathrm{⋮<br><br>}\\ c_M^n\end{array}\right)+\mathrm{\Delta <br><br>}\times <br><br>{\left(u_n-{\hat{u}}_n\right)}^{*}<br><br>\left(\begin{array}{c}{u}_{n-1}\\ u_{n-2}\\ \mathrm{⋮<br><br>}\\ u_{n-M}\end{array}\right)$

where Δ

is the adaptation step size,

μ

is a tap leakage constant,

u_n, u<sub>n−

1</sub>, . . . , u<sub>n−

M</sub> are the actual noise samples at time cycles n, n−

1, . . . , n−

M, and

û

_n is the amount of predicted noise at time cycle n.

32. The device of claim 27, further comprising:

an equalizer having noise-independent coefficients and configured to compensate for inter-symbol interference in the digital data.

33. The device of claim 32, where an output of the equalizer is coupled to the first subtractor.

34. The device of claim 32, where the equalizer comprises a zero-forcing equalizer.

35. The device of claim 32, further comprising:

another adaptation circuit configured to adapt coefficients of the equalizer.

36. The device of claim 35, where the other adaptation circuit is configured to adapt coefficients of the equalizer according to a least-means-square (LMS) algorithm.

37. The device of claim 27, further comprising:

a storage unit configured to receive and store past actual noise samples (u<sub>n−

i</sub>) from the second subtractor, where i designates past time cycles.

38. The device of claim 26, wherein the adaptation circuit is further configured to minimize the mean square error (|û

_n−

u_n|²) of the actual noise sample (u_n).

39. The device of claim 26, where the device includes a termination system for a plurality of transmitters in a time division multiple access (TDMA) network, where the device reduces the noise in digital data received from at least one of said transmitters, and further comprising:

a control unit configured to;

reserve time slots in which no transmitter of the plurality of transmitters is allowed to transmit towards the system, and

control the adaptation circuit so that the predictor coefficients of the noise predictor circuit are adapted during the reserved time slots.

40. A method, comprising:

receiving a sample (s_n) of digital data, where the sample (s_n) comprises useful data (x_n) and an actual noise sample (u_n);

determining an amount of predicted noise (û

_n) at a time cycle n in the sample (s_n) using predictor coefficients;

determining the predictor coefficients for a future time cycle n+1 based on an adaptation step size, the predicted noise (û

_n) at a time cycle n, actual noise samples at time cycles n, n−

1, n−

2, . . . n−

M, and predictor coefficients from time cycle n, where determining the predictor coefficients is further based on a multiplication of the adaptation step size with a value associated with a difference between the actual noise sample (u_n) at time sample n and the predicted noise (û

_n); and

generating a decided symbol (d_n) representative of the useful data (x_n) in the sample (s_n) based on the predicted noise (û

_n).

41. The method of claim 40, further comprising:

subtracting the amount of predicted noise (û

_n) from the sample (s_n) of the digital data to produce subtracted data (s_n−

û

_n);

comparing the subtracted data (s_n−

û

_n) with a set of thresholds to generate the decided symbol (d_n) representative of the useful data (x_n) in the sample (s_n); and

subtracting the decided symbol (d_n) from the sample (s_n) to produce the actual noise sample (u_n).

42. The method of claim 40, where the predictor coefficients (c_kⁿ⁺¹) are determined for the future time cycle n+1 according to:

$\left(\begin{array}{c}{c}_1^{n+1}\\ c_2^{n+1}\\ \mathrm{⋮<br><br>}\\ c_M^{n+1}\end{array}\right)=\left(\begin{array}{c}{c}_1^n\\ c_2^n\\ \mathrm{⋮<br><br>}\\ c_M^n\end{array}\right)+\mathrm{\Delta <br><br>}\times <br><br>{\left(u_n-{\hat{u}}_n\right)}^{*}<br><br>\left(\begin{array}{c}{u}_{n-1}\\ u_{n-2}\\ \mathrm{⋮<br><br>}\\ u_{n-M}\end{array}\right)$

where Δ

is the adaptation step size,

u_n, u<sub>n−

1</sub>, . . . , u<sub>n−

M</sub> are the actual noise samples at time cycles n, n−

1, . . . , n−

M, and

û

_n is the amount of predicted noise at time cycle n.

43. The method of claim 42, where the amount of predicted noise û

_n is determined according to;

${\hat{u}}_n=\underset{k=1}{\overset{M}{\sum <br><br>}}<br><br>c_k^{n^{*}}<br><br>u_{n-k}$

where c_kⁿ are the predictor coefficients at the time cycle n.

44. The method of claim 40, where the predictor coefficients for the future time cycle n+1 are further determined using a tap leakage technique.

45. The method of claim 44, where the predictor coefficients (c_kⁿ⁺¹) for the future time cycle n+1 are determined according to:

$\left(\begin{array}{c}{c}_1^{n+1}\\ c_2^{n+1}\\ \mathrm{⋮<br><br>}\\ c_M^{n+1}\end{array}\right)=\left(1-\mathrm{\mu <br><br>}\times <br><br>\mathrm{\Delta <br><br>}\right)<br><br>\left(\begin{array}{c}{c}_1^n\\ c_2^n\\ \mathrm{⋮<br><br>}\\ c_M^n\end{array}\right)+\mathrm{\Delta <br><br>}\times <br><br>{\left(u_n-{\hat{u}}_n\right)}^{*}<br><br>\left(\begin{array}{c}{u}_{n-1}\\ u_{n-2}\\ \mathrm{⋮<br><br>}\\ u_{n-M}\end{array}\right)$

where Δ

is the adaptation step size,

μ

is a tap leakage constant,

u_n, u<sub>n−

1</sub>, . . . , u<sub>n−

M</sub> are the actual noise samples at time cycles n, n−

1, . . . , n−

M, and

û

_n is the amount of predicted noise at time cycle n.

46. The method of claim 40, where the sample (s_n) of digital data is received in a termination system from a plurality of transmitters in a time division multiple access (TDMA) network and further comprising:

reserving time slots in which no transmitter of the plurality of transmitters is allowed to transmit towards the termination system; and

controlling the determination of the predictor coefficients so that the predictor coefficients are determined during the reserved time slots.