U.S. patent application number 13/360560 was filed with the patent office on 2012-05-24 for detection and mitigation of temporary (bursts) impairments in channels using scdma.
This patent application is currently assigned to BROADCOM CORPORATION. Invention is credited to Thomas J. Kolze.
Application Number | 20120127962 13/360560 |
Document ID | / |
Family ID | 30772572 |
Filed Date | 2012-05-24 |
United States Patent
Application |
20120127962 |
Kind Code |
A1 |
Kolze; Thomas J. |
May 24, 2012 |
DETECTION AND MITIGATION OF TEMPORARY (BURSTS) IMPAIRMENTS IN
CHANNELS USING SCDMA
Abstract
Systems and methods are disclosed for detecting and mitigating
temporary high-level impairments, in a communications channel, and
subsequently, mitigating the deleterious effects of the dynamic
impairments. The system includes a transmitter and a receiver. The
transmitter is adapted to transmit at least one set of modulated
signals. The receiver is adapted to receive the at least one set of
modulated signals and mitigate temporary high-level impairment in
the at least one set of modulated signals using at least one error
vector received during the temporary high-level impairment.
Inventors: |
Kolze; Thomas J.; (Phoenix,
AZ) |
Assignee: |
BROADCOM CORPORATION
IRVINE
CA
|
Family ID: |
30772572 |
Appl. No.: |
13/360560 |
Filed: |
January 27, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12535440 |
Aug 4, 2009 |
8107355 |
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13360560 |
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10237853 |
Sep 9, 2002 |
7570576 |
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12535440 |
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10000415 |
Nov 2, 2001 |
7308050 |
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10237853 |
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60402776 |
Aug 12, 2002 |
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60296884 |
Jun 8, 2001 |
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Current U.S.
Class: |
370/335 ;
375/346 |
Current CPC
Class: |
H04B 1/71055 20130101;
H04B 1/71072 20130101; H04B 2201/709709 20130101; H04N 21/6168
20130101; H04L 1/0003 20130101; H04L 1/005 20130101; H04L 1/0009
20130101; H04B 1/7102 20130101; H04L 1/0045 20130101; H04B 1/7103
20130101; H04B 1/71 20130101; H04L 1/0057 20130101 |
Class at
Publication: |
370/335 ;
375/346 |
International
Class: |
H04B 7/216 20060101
H04B007/216; H04W 92/00 20090101 H04W092/00; H03D 1/04 20060101
H03D001/04 |
Claims
1. An apparatus, comprising: an input to receive a signal including
a plurality of concurrently modulated symbols having temporary
impairment; and a processor to mitigate the temporary impairment in
accordance with processing based upon a function of error vectors
associated with the signal thereby generating a processed
signal.
2. The apparatus of claim 1, further comprising: at least one
additional apparatus including a transmitter to transmit the signal
including the plurality of concurrently modulated symbols via a
communication channel to the apparatus; and wherein: during
transmission via the communication channel, the signal including
the plurality of concurrently modulated symbols incurring the
temporary impairment; the temporary impairment being temporary
high-level or severe impairment; and the signal being a synchronous
code division multiple access (SCDMA) signal.
3. The apparatus of claim 1, wherein: the input to receive a
plurality of signals, including the signal including the plurality
of concurrently modulated symbols having temporary impairment; and
the function of error vectors associated with each signal of the
plurality of signals.
4. The apparatus of claim 1, wherein: the processor to mitigate the
temporary impairment by weighting the plurality of concurrently
modulated symbols with at least one modified likelihood based upon
the function of error vectors associated with the signal.
5. The apparatus of claim 1, wherein: the processor to mitigate the
temporary impairment by erasing the plurality of concurrently
modulated symbols based upon the function of error vectors
associated with the signal.
6. The apparatus of claim 1, wherein: the function of error vectors
associated with the signal being a function of error powers
associated with the signal; and the processor to compare a sum of
the error powers with a threshold.
7. The apparatus of claim 1, wherein: the processor to discard at
least one of the error vectors having an extreme value.
8. The apparatus of claim 1, further comprising: a notched filter
to process the processed signal to mitigate at least one additional
deleterious effect within the signal or the processed signal.
9. An apparatus, comprising: an input to receive a signal including
a plurality of concurrently modulated symbols having temporary
impairment, wherein the temporary impairment including narrowband
interference and time-domain burst noise; a processor to mitigate
the time-domain burst noise in accordance with processing based
upon a function of error vectors associated with the signal thereby
generating a processed signal; and a notched filter to process the
processed signal to mitigate the narrowband interference.
10. The apparatus of claim 9, wherein: at least one coefficient of
the notched filter being adapted for use in at least one subsequent
filtering operation.
11. The apparatus of claim 9, further comprising: at least one
additional apparatus including a transmitter to transmit the signal
including the plurality of concurrently modulated symbols via a
communication channel to the apparatus; and wherein: during
transmission via the communication channel, the signal including
the plurality of concurrently modulated symbols incurring the
temporary impairment; the temporary impairment including temporary
high-level or severe impairment; and the signal being a synchronous
code division multiple access (SCDMA) signal.
12. The apparatus of claim 9, wherein: the processor to mitigate
the time-domain burst noise by weighting the plurality of
concurrently modulated symbols with at least one modified
likelihood based upon the function of error vectors associated with
the signal.
13. The apparatus of claim 9, wherein: the processor to mitigate
the time-domain burst noise by erasing the plurality of
concurrently modulated symbols based upon the function of error
vectors associated with the signal.
14. The apparatus of claim 9, wherein: the function of error
vectors associated with the signal being a function of error powers
associated with the signal; and the processor to compare a sum of
the error powers with a threshold.
15. An apparatus, comprising: an input to receive a signal
including a plurality of concurrently modulated symbols having
temporary impairment; and a processor to: make a plurality of
decisions on the plurality of concurrently modulated symbols;
re-modulate the plurality of decisions thereby generating a
plurality of remodulated symbols; mitigate the temporary impairment
in accordance with processing based upon a function of error
vectors associated with the plurality of remodulated symbols
thereby generating a processed signal.
16. The apparatus of claim 15, further comprising: at least one
additional apparatus including a transmitter to transmit the signal
including the plurality of concurrently modulated symbols via a
communication channel to the apparatus; and wherein: during
transmission via the communication channel, the signal including
the plurality of concurrently modulated symbols incurring the
temporary impairment; the temporary impairment being temporary
high-level or severe impairment; and the signal being a synchronous
code division multiple access (SCDMA) signal.
17. The apparatus of claim 15, wherein: the processor to mitigate
the temporary impairment by weighting the plurality of concurrently
modulated symbols with at least one modified likelihood based upon
the function of error vectors associated with the signal.
18. The apparatus of claim 15, wherein: the processor to mitigate
the temporary impairment by erasing the plurality of concurrently
modulated symbols based upon the function of error vectors
associated with the signal.
19. The apparatus of claim 15, wherein: the function of error
vectors associated with the signal being a function of error powers
associated with the signal; and the processor to compare a sum of
the error powers with a threshold.
20. The apparatus of claim 15, further comprising: a notched filter
to process the processed signal to mitigate at least one additional
deleterious effect within the signal or the processed signal.
Description
CROSS REFERENCE TO RELATED PATENTS/PATENT APPLICATIONS
Continuation Priority Claim, 35 U.S.C. .sctn.120
[0001] The present U.S. Utility patent application claims priority
pursuant to 35 U.S.C. .sctn.120, as a continuation, to the
following U.S. Utility patent application which is hereby
incorporated herein by reference in its entirety and made part of
the present U.S. Utility patent application for all purposes:
[0002] 1. U.S. Utility patent application Ser. No. 12/535,440,
entitled "Detection and mitigation of temporary (bursts)
impairments in channels using SCDMA," (Attorney Docket No.
BP1950.1C1), filed 8-04-2009, pending, and scheduled to be issued
as U.S. Pat. No. 8,107,355 on 01-31-2012 (as indicated in an ISSUE
NOTIFICATION mailed on 01-11-2012) which claims priority pursuant
to 35 U.S.C. .sctn.120, as a continuation, to the following U.S.
Utility patent application which is hereby incorporated herein by
reference in its entirety and made part of the present U.S. Utility
patent application for all purposes:
[0003] 2. U.S. Utility patent application Ser. No. 10/237,853,
entitled "Detection and mitigation of temporary (bursts)
impairments in channels using SCDMA," (Attorney Docket No. BP1950.1
or 13408US02), filed 09-09-2002, now U.S. Pat. No. 7,570,576 issued
on 08-04-2009, which claims priority pursuant to 35 U.S.C.
.sctn.119(e) to the following U.S. Provisional patent application
which is hereby incorporated herein by reference in its entirety
and made part of the present U.S. Utility patent application for
all purposes: [0004] 2.1. U.S. Provisional Patent Application Ser.
No. 60/402,776, entitled "Detection and mitigation of temporary
(bursts) impairments in channels using SCDMA," (Attorney Docket No.
BP1950.1 or 13408US01), filed 08-12-2002, now expired.
[0005] The U.S. Utility patent application Ser. No. 10/237,853 also
claims priority pursuant to 35 U.S.C. .sctn.120, as a
continuation-in-part (CIP), to the following U.S. Utility patent
application which is hereby incorporated herein by reference in its
entirety and made part of the present U.S. Utility patent
application for all purposes:
[0006] 3. U.S. Utility patent application Ser. No. 10/000,415,
entitled "Detection and mitigation of temporary impairments in a
communication channel," (Attorney Docket No. BP1950 or 13119US02),
filed 11-02-2001, now U.S. Pat. No. 7,308,050 issued on 12-11-2007,
which claims priority pursuant to 35 U.S.C. .sctn.119(e) to the
following U.S. Provisional patent application which is hereby
incorporated herein by reference in its entirety and made part of
the present U.S. Utility patent application for all purposes:
[0007] 3.1. U.S. Provisional Patent Application Ser. No.
60/296,884, entitled "Detection and mitigation of temporary
impairments in a communication channel," (Attorney Docket No.
BP1950 or 13119US01), filed 06-08-2001, now expired.
BACKGROUND OF THE INVENTION
[0008] 1. Technical Field of the Invention
[0009] The present invention relates to communications channels,
all of which are inherently limited in their capacity (or rate) of
information transfer by channel impairments. More specifically, the
present invention relates to the detection and mitigation of
temporary impairments (temporary high-level impairments for
example) in information transfer between a plurality of Cable
Modems (alternatively referred to as "CM") and a Cable Termination
System (alternatively referred to as "CMTS" or "headend").
[0010] 2. Description of Related Art
[0011] Communication systems are subjected to impairments,
generally of a brief or transitory duration. One example of such
impairment is often referred to by the generic term "noise." Noise
sometimes emanates for example, from within electrical components
themselves, such as amplifiers and even passive resistors. Another
example of impairment is referred to as "interference," which is
usually taken to be some unwanted manmade emission, from another
communications system such as a radio or from switching circuits in
a home or automobile for example. "Distortion" is a yet another
example of such impairment, and includes linear distortion in the
channel, such as pass-band ripple or non-flat group delay for
example, and nonlinear distortion, such as compression in an
overdriven amplifier for example. It is contemplated that there are
many other types of impairments that may also adversely affect
communications in a channel.
[0012] Often, such impairments may be dynamic in nature. In many
cases, the impairment may be at one level of severity most of the
time. In this instance, the communications system may be designed
or optimized in some fashion to operate at that specific level of
impairment. Occasionally, however, one or more of impairments may
become so severe as to preclude the operation of such
communications system optimized for the more ordinary level of
impairments.
[0013] Previously, when a large interference or burst of noise
occasionally impinged upon the receiver (a CM for example), such
large out-of-the ordinary bursts of received power is simply
blanked out. Often, analog processing means are used, almost at, if
not right at, the receiver input. This may be done especially to
protect CMs or other sensitive receiver front-ends from damage.
While this technique may provide some benefit in circumstances
where the noise or interference power dwarfs the signal-of-interest
power, it does not protect against the many other impairments that
have power more on the order of the signal-of-interest power (or
even much less). Thus, blanking does not, by itself, provide the
receiver with a means to improve its overall performance in the
presence of the lost information, i.e., the information content
concurrent with the large noise burst.
[0014] One known technique, a forward error correction technique
(alternatively referred to as "FEC") has been applied, even
unknowingly, to solve this problem. FEC techniques incorporate
soft-decision decoding, such as is common with convolutional error
correction codes and the Viterbi decoding algorithm. In such
correction techniques, as the error power in the received signal
increases, such increase is passed directly into the decision
process.
[0015] Such encoding and decoding techniques have been in common
practice for years, and are widely applied without thought to
temporary fidelity changes in the channel. Fortunately, in the
event of a change in the channel fidelity, the soft-decision
decoding takes into consideration the larger error power in making
signal decisions. However, unfortunately, often with a change in
channel conditions, there is duration of multiple symbol intervals
(in a digital communications system for example) where the
degradation persists. During this time some symbols may be so
severely erred that they actually appear close to another possible
but incorrect symbol. In such event, the soft-decision decoder
actually "thinks" it has received a low error power, and may rate
the wrong signal with a high confidence. This becomes much more
likely as the constellation density (of a QAM constellation for
example) is increased for high rate communications.
[0016] Additional techniques, such as a Time Division Multiple
Access technique (alternatively referred to as "TDMA") have been
applied to solve this problem. In this technique, one or more
carrier frequencies are shared among a plurality of CMs. Known
standards, DOCSIS 1.0 and 1.1 for example, each of which are
incorporated herein by reference in their entirety, define the
physical layer, and additional layers, in which a plurality of CMs
transmit data upstream to and receive data downstream from the CMTS
or headend. In this technique, each upstream carrier frequency or
channel assignment is generally shared by a plurality of CMs, each
being granted time slots wherein they may use the channel. These
grants are allocated and made known to the CMs via the downstream
broadcast transmissions. Some of the grants only enable a single CM
to transmit, while other time slot grants are in contention mode.
That is some, or all, of the CMs may attempt to use the grant.
However, if more than one CM attempts to use a grant in the
contention mode, all the CMs will likely be unsuccessful in channel
use.
[0017] Yet another technique, such as a direct-sequence
spread-spectrum modulation technique discussed by J. Young and J.
Lehnert, in their paper titled "Analysis of DFT-Based Frequency
Excision Algorithms for Direct-Sequence Spread-Spectrum
Communications," IEEE Trans. Comm., vol. 46, pp. 1076-1087, August
1998, the complete subject matter of which is incorporated herein
by reference in its entirety, has also been applied to this
problem. In this technique, frequency excision is used to eliminate
narrow-band energy, thus enhancing the capacity of direct-sequence
spread-spectrum modulation to reject narrow-band interference.
However, this disclosed technique focuses on particular waveforms
having energy concentrated about a narrow band.
[0018] Yet still another technique, such as a Code-Division
Multiple Access technique (alternatively referred to as "CDMA")
discussed by M. Lops, G. Ricci and A. Tulino, in their paper titled
"Narrow-Band-Interference Suppression in Multi-user CDMA Systems,"
IEEE Trans. Comm., vol. 46, pp. 1163-1175, September 1998, the
complete subject matter of which is incorporated herein by
reference in its entirety, has also been applied to this problem.
In this technique, a decision is made regarding the bit(s)
transmitted by each user over a communication system. This decision
is based on the projection of the observables on to the orthogonal
complement to the subspace spanned by the other users' signatures
and the narrow-band interference. The disclosed technique
recognizes that blanking and iterative processing may be performed
with an orthogonal basis set decomposition of the frequency
domain.
[0019] Yet still a further technique, such as a Synchronous Code
Division Multiple Access technique (alternatively referred to as
"SCDMA") comprises a spreading technique to transmit symbols at the
same time on the same frequency. More specifically, this technique
may be used, in one embodiment, with a DOCSIS 2.0 physical layer
standard (alternatively referred to as the "DOCSIS standard"),
which is incorporated herein by reference in its entirety. The
DOCSIS standard defines the physical layers in which pluralities of
CMs transmit data upstream to and receive data downstream from the
CMTS or headend.
[0020] It is contemplated that in SCDMA, the spreading codes may be
cyclical shifts of one 127 chip spreading code, plus one additional
chip. Thus, the spreading codes are nearly cyclical shifts of one
another. For such SCDMA technique to work efficiently, all the
spreading codes must be synchronized as they arrive at the
receiver. Timing misalignments may result in inter code
interference (alternatively referred to as "ICI"), which may
degrade signal performance. Various impairments, interference,
distortion or noise in the channel may also degrade signal
performance. In one embodiment, special receiver techniques may be
employed to limit or mitigate the degradation caused by such
channel performance.
[0021] Further limitations and disadvantages of conventional and
traditional approaches will become apparent to one of skill in the
art, through comparison of such systems with the present invention
as set forth in the remainder of the present application with
reference to the drawings.
BRIEF SUMMARY OF THE INVENTION
[0022] Features of the present invention related to systems and
methods for detecting temporary high-level (i.e., severe)
impairments, such as noise or interference, for example, in a
communications channel, and subsequently, mitigating the
deleterious effects of the dynamic impairments. In one embodiment,
the system includes a transmitter adapted to transmit a plurality
of modulated signals and a receiver. The receiver is adapted to
receive the signals and mitigate severe impairments in the signals
using a multitude of error vectors derived during such severe
impairments.
[0023] Detection and mitigation of temporary impairments is
disclosed in commonly assigned application Ser. No. 10/000,415
filed Nov. 2, 2001, titled "Detection and Mitigation of Temporary
Impairments in a Communications Channel", which is incorporated
herein by reference in its entirety (alternatively referred to as
the "Detection and Mitigation application"). One embodiment of the
present invention uses a system similar to that disclosed in the
Detection and Mitigation application that is now applied to SCDMA
or other modulations, where many chips are impacted by such severe
impairments and the receiver is better off erasing (or weighting
with low likelihood) the whole set of symbols.
[0024] Chip blanking and processing to mitigate impulse noise is
disclosed in commonly assigned application Ser. No. 10/136,059
filed Apr. 30, 2002, titled "Chip Blanking and Processing in SCDMA
to Mitigate Impulse and Burst Noise and/or Distortion"
(alternatively referred to as the "Blanking and Processing
application"). In one embodiment of the present invention,
detecting the higher impairment levels during a set of symbols can
be used to trigger chip blanking similar to that disclosed in the
Blanking and Processing application to attempt impairment
mitigation via those techniques as well as using the changed
likelihood weightings similar to that disclosed in the Detection
and Mitigation application.
[0025] In other words, this invention is an application of the
Detection and Mitigation application to modulations where
pluralities of symbols are concurrently transmitted through the
channel. Improved performance may result by properly adapting the
detection and mitigation teachings to a plurality of concurrent
symbols. However, this invention may further incorporate and
interact with the techniques of the Chip Blanking and Processing
Application. This invention together with the techniques of the
Chip Blanking and Processing Application provide beneficial
synergy. As an embodiment, if the Chip Blanking and Processing
techniques fail to provide a minimum error power estimate (which is
explained in the detailed description) or if too many chips are
required to determine blanking, then the erasing (or weighting with
low likelihood) of the whole set of symbols may be invoked, as
taught by the Detection and Mitigation application.
[0026] One embodiment of the present invention relates to systems
adapted to detect and mitigate temporary high-level impairments, in
a communications channel, and subsequently, mitigate the
deleterious effects of the dynamic impairments. The system includes
a transmitter and a receiver. The transmitter is adapted to
transmit at least one set of modulated signals (SCDMA or other
modulated signals). The receiver is adapted to receive the at least
one set of modulated signals and mitigate temporary high-level
impairment in the at least one set of modulated signals using at
least one error vector received during the temporary high-level
impairment.
[0027] In another embodiment, the receiver may make hard decisions
with respect to the set of modulated signals and assign likelihood
weightings thereto. The system may erase (or weigh low likelihood)
the set of modulated signals if a sum of an error power exceeds a
predetermined threshold. Further, the receiver may erase (or weigh
low likelihood) the set of modulated signals based upon a function
of error powers of all the modulated signals. Further, the receiver
may discard at least one error vector having an extreme value.
[0028] In another embodiment, the receiver may further comprise,
alone or in some combination, a demodulator, a plurality of matched
filters, a plurality of slicers, a calculator and a hard decision
block. The demodulator may be adapted to demodulate the modulated
signals. The plurality of matched filters may be adapted to output
a plurality of soft-decision values. The plurality of slicers may
be adapted to output a plurality of hard-decision estimates. The
calculator may be adapted to output at least one error vector,
wherein the calculator uses at least one soft-decision value and at
least one initial hard-decision estimate to determine the at least
one error vector. The decision block may be adapted to determine a
hard-decision value.
[0029] In another embodiment, the system receiver may be adapted to
make preliminary hard decisions on all the signals. Further the
receiver may perform at least one of the following including
demodulating, filtering, blanking, making assessments of distortion
characteristics, matched filtering to an ideal signaling waveform
and remodulating, and making at least one distortion decision and
performing any additional blanking and filtering as driven by the
distortion decision. In this embodiment, the receiver may make hard
decisions with respect to the set of modulated signals and assign
likelihood weightings thereto. The system may erase (or weigh low
likelihood) the set of modulated signals if a sum of an error power
exceeds a predetermined threshold. Further, the receiver may erase
(or weigh low likelihood) the set of modulated signals based upon a
function of error powers of all the modulated signals. Further, the
receiver may discard at least one error vector having an extreme
value. In this embodiment, the receiver may further comprise, alone
or in some combination, a demodulator, a plurality of matched
filters, a plurality of slicers, a calculator and a hard decision
block. The demodulator may be adapted to demodulate the modulated
signals. The plurality of matched filters may be adapted to output
a plurality of soft-decision values. The plurality of slicers may
be adapted to output a plurality of hard-decision estimates. The
calculator may be adapted to output at least one error vector,
wherein the calculator uses at least one soft-decision value and at
least one initial hard-decision estimate to determine the at least
one error vector. The decision block may be adapted to determine a
hard-decision value.
[0030] In another embodiment, the system receiver may make at least
one subsequent hard decision with respect to the set of modulated
signals and repair any blanking damage and filtering using the at
least one subsequent hard decision.
[0031] Still another embodiment relates to a method of performing
impairment mitigation on at least one set of modulated signals in a
communications system. This method comprises mitigating a temporary
high-level using at least one error vector received during the
temporary high level impairment. This method may further comprise
making preliminary hard decisions on all the signals and performing
at least one of the following including demodulating, filtering,
blanking, making assessments of distortion characteristics, matched
filtering to an ideal signaling waveform and remodulating, and
making at least one distortion decision and performing any
additional blanking and filtering as driven by the distortion
decision.
[0032] Such methods may comprise making at least one subsequent
hard decision with respect to the set of modulated signals and
repairing any blanking damage and filtering using the at least one
subsequent hard decision. Other methods may comprise generating at
least one error estimate, where generating the at least one error
estimate may comprise determining at least one constellation point
closest to each of the symbols, determining a distance between the
symbols and their nearest constellation point; and squaring the
distances.
[0033] Still another method relates to a method of impairment
mitigation in a communications system. This method comprises making
a preliminary decision on all signals in a set of modulated signals
and remodulating the signals. Distortion is determined for each of
the signals using the remodulated signals.
[0034] These and other advantages and novel features of the present
invention, as well as details of an illustrated embodiment thereof,
will be more fully understood from the following description and
drawings.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0035] FIG. 1 illustrates a block diagram of a generic
communication system that may be employed in connection with the
present invention.
[0036] FIG. 2 illustrates a block diagram of one embodiment of an
impairment mitigation system in accordance with the present
invention.
[0037] FIG. 3 illustrates a flow diagram of one embodiment of a
method that may be performed using the system of FIG. 2, in
accordance with the present invention.
[0038] FIG. 4 illustrates a flow diagram of another embodiment of a
method that may be performed using the system of FIG. 2, in
accordance with the present invention.
[0039] FIGS. 5A and 5B illustrate flow diagrams of specific
embodiments of methods of impairment mitigation in accordance with
the present invention.
[0040] FIG. 6 illustrates a block diagram of another embodiment of
an impairment mitigation system in accordance with the present
invention.
[0041] FIGS. 7A & 7B illustrate a flow diagram of one specific
embodiment of a method of impairment mitigation for use in
connection with digital communications in accordance with the
present invention.
[0042] FIGS. 8A & 8B illustrate a flow diagram of one
embodiment of a method of impairment mitigation used in connection
with digital communications in accordance with the present
invention.
[0043] FIG. 9 illustrates a flow diagram of one embodiment of a
method that uses a fidelity metric to modify branch metrics in the
decoding process, in accordance with the present invention.
[0044] FIG. 10 illustrates a block diagram of one embodiment of an
impairment mitigation system that uses preliminary decoding in
generating error power estimates, in accordance with the present
invention.
[0045] FIG. 11 illustrates a flow diagram illustrating one
embodiment of a method of impairment mitigation that may be
employed using the system of FIG. 10 in accordance with one
embodiment of the present invention.
[0046] FIG. 12 illustrates a high-level flow diagram illustrating
one embodiment of an alternate method of impairment mitigation that
may be employed using a system similar to that illustrated in FIG.
10 in accordance with the present invention.
[0047] FIGS. 13A & 13B illustrate a flow diagram illustrating
an alternate embodiment of a method of impairment mitigation
similar to that illustrated in FIG. 12 that may be employed using a
system similar to that illustrated in FIG. 10 in accordance with
the present invention.
[0048] FIG. 14 illustrates a block diagram of one embodiment of a
SCDMA receiver in accordance with the present invention.
[0049] FIG. 15 illustrates a flow diagram illustrating one
embodiment of a method for detecting temporary (i.e., bursts) high
levels of distortion and mitigating such distortion using a
receiver similar to that illustrated in FIG. 14 in accordance with
the present invention.
[0050] FIG. 16 illustrates a block diagram of an embodiment of an
impairment mitigation system in accordance with the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
[0051] The following description is made with reference to the
appended figures.
[0052] One embodiment of the present invention relates to a
spreading technique to transmit a plurality of symbols at the same
time on the same frequency. More specifically, one embodiment of
the present invention relates to an SCDMA technique for mitigating
temporary, high levels of impairments of one up to several chips in
communication channels used, in one embodiment, with the DOCSIS
standard. It is contemplated that, while an SCDMA spreading or
modulating technique is discussed, any spreading or modulating
techniques such as OFDM, DMT, and SWMT for example, are
contemplated.
[0053] According to one embodiment of the present invention,
temporary impairments or other distortion-inducing mechanisms of
duration of one up to several chips (16 chips for example) may be
detected. It is contemplated that, while such impairment of 16
chips in duration is discussed; a severe impairment (even of a
shorter duration) may be detected and mitigated using the present
invention. Means are provided to detect such temporarily high
amount of impairment in the symbols and improve the reliability of
the symbol decisions even in the presence of such impacted or
distorted chips. More specifically, one embodiment of the present
invention relates to means for detecting at least one up to several
chip(s) with such severe impairment as provided below. In the
embodiments provided below, it is contemplated that a chip plays
the roll of a symbol, signal, waveform, etc., although other
embodiments are also contemplated as discussed.
[0054] In one embodiment of the present invention using SCDMA
modulation, up to 128 spreading codes are available for modulating
each upstream-transmitted symbol. In this embodiment, one or more
sets of symbols, where each symbol is comprised of one up to 128
symbols, may be transmitted simultaneously, each symbol using its
own spreading code. Each spreading code consists of a sequence of
+1 or -1 valued chips, such that there are 128 such chips in each
spreading code. In this embodiment, the symbol amplitude and angle
are modulated using a vector, applying the vector or its additive
inverse (i.e., 180 degree rotation) to the symbol.
[0055] In one embodiment, the spreading codes are orthogonal if
perfectly time-aligned, and thus the up to 128 symbols in each set
of symbols will not interfere with each other, even though they are
transmitted at the same time on the same channel. For example, two
waveforms are orthogonal to each other if, after multiplying them
by each other and integrating, the result of the integration is
zero. In SCDMA modulation used with one embodiment of the present
invention, at least two but up to and including 128 spreading codes
may be used at one time. These spreading codes may be allocated to
one CM for example, such that that CM is granted all the spreading
codes (128 for example), up to and including the spreading codes
being allocated to 64 different CMs for example, such that two
spreading codes are granted to each CM. QAM symbols of two bits per
symbol and more are spread with the assigned codes, one spreading
code per QAM symbol, although other arrangements are
contemplated.
[0056] In one embodiment using SCDMA, the spreading codes are
cyclical shifts of one 127-chip spreading code, except for one
additional chip. Thus, in this embodiment, the spreading codes are
nearly cyclical shifts of one another. Furthermore, for SCDMA
modulation to work efficiently, all the spreading codes should be
synchronized as they arrive at the receiver.
[0057] FIG. 1 illustrates a block diagram of a generic
communication system that may be employed in connection with one
embodiment of the present invention. The system comprises a first
communication node 101, a second communication node 111, and at
least one channel 109 that communicatively couples the nodes 101
and 111. The communication nodes may be, for example, cable modems,
DSL modems or any other type of transceiver device that transmits
or receives data over one or more channels (generally referred to
as CMs).
[0058] The first communication node 101 comprises a transmitter
105, a receiver 103 and a processor 106. The processor 106 may
comprise, for example, a microprocessor. The first communication
node 101 communicates with or is communicatively coupled to a user
100 (e.g., a computer) via communication link 110, and to the
channel 109 via communication links 107 and 108. Of course,
communication links 107 and 108 may be combined into a single
communication link.
[0059] Similarly, the second communication node 111 comprises a
transmitter 115, a receiver 114 and a processor 118. The processor
118, like processor 106, may comprise, for example, a
microprocessor. The second communication node 111 likewise is
communicatively coupled to the at least one channel 109 via
communication links 112 and 113. Again, like communication links
107 and 108, the communication links 112 and 113 may be combined
into a single communication link. The communication node 111 may
also be communicatively coupled to a user 120 (again a computer,
for example) via communication link 121. In the case when
communication node 111 is a headend, for example, user 120 may not
be present.
[0060] During operation of the illustrated embodiment of FIG. 1,
the user 100 may communicate information to the user 120 (or the
headend) using the first communication node 101, the at least one
channel 109 and the second communication node 111. Specifically,
the user 100 communicates the information to the first
communication node 101 via communication link 110. The information
is transformed in the transmitter 105 to match the restrictions
imposed by the at least one channel 109. The transmitter 105 then
communicates the information to the at least one channel 109 via
communication link 107.
[0061] The receiver 114 of the second communication node 111
receives, via communication link 113, the information from the at
least one channel 109 and transforms it into a form usable by the
user 120. Finally, the information is communicated from the second
communication node 111 to the user 120 via the communication link
121.
[0062] Communication of information from user 120 to user 100 may
also be achieved in a similar manner. In either case, the
information transmitted/received may also be processed using the
processors 106/118.
[0063] FIG. 2 illustrates a block diagram of an impairment
mitigation system 200 in accordance with one embodiment of the
present invention. The system 200 may be contained, for example, in
one or both of the communication nodes of FIG. 1. Error power
estimates may be generated for analog modulations. A receiver 201
receives an input at input 203 of either noise (when no signal is
present) or a signal with time varying distortion and/or noise, for
example. The receiver 201 uses the input to generate error power
estimates, and may do so using a sequence of bits using a sequence
or set of symbols, in a digital communications example. A sliding
window 205 receives the error power estimates. The error power
estimates are processed in a fidelity processor 207 and a metric
for channel fidelity is continuously generated as the window
progresses (i.e., over time). The behavior of the metric versus
time may be catalogued (see catalogue 209) and/or analyzed and used
to optimize the transmission waveform as provided in the Detection
and Mitigation application. The behavior of the metric versus time
may also be used to enhance receiver performance in real-time,
near-real time, or even in a post-reception, post-processing
mode.
[0064] A delay 208 between the input error power estimates of the
window 205 and the corresponding channel fidelity metric is known
for a given fidelity processor, and is provided back (made known)
to a remainder of the system. The system uses the evolving fidelity
metric in its processing, which may be aided by soft-decisions
designated 211. Soft-decisions comprise, for example, erasure
decoding or standard soft-decision decoding, such as Viterbi
decoding. In any case, the receiver outputs an estimate of the
transmitted signal (reference numeral 213).
[0065] While FIG. 2 illustrates a system having some components and
functionality located outside of the receiver, it is contemplated
that such system may have additional components or functionality
located within the receiver, or may in fact be entirely contained
within the receiver. In addition, it is also contemplated that the
estimation of the error power and the processing shown as being
performed within the receiver of FIG. 2 may instead be performed
outside of the receiver.
[0066] FIG. 3 illustrates a flow diagram of one embodiment of a
method that may be performed using the system of FIG. 2, in
accordance with the present invention. In one embodiment of the
method, the error power of an input to the system is estimated as
illustrated by block 301, where the input comprises a symbol, a
plurality of symbols or one or more sets of symbols, each such set
comprising one or more symbols. A fidelity metric is determined,
using the error power estimate as illustrated by block 303. The
determined fidelity metric is then used to decode the input as
illustrated by block 305. The method illustrated in FIG. 3 may be
employed on a limited basis, for example only during the presence
of the signal of interest for example, or may be employed
continuously. In other words, the method specifically illustrated
in FIG. 3 may be employed in a continuous loop type fashion, either
for a limited or extended period of time. In either case, the error
power of the input is estimated over time, and fidelity metrics are
determined (each using one or more error power estimates of the
input) and used to decode the input over time.
[0067] FIG. 4 illustrates a flow diagram of another embodiment of a
method that may be performed using the system of FIG. 2, in
accordance with one embodiment of the present invention. In one
embodiment, the error power of an input to the system is estimated
as illustrated by block 401. A fidelity metric is determined, using
the error power estimate as illustrated by block 403. The
determined fidelity metric is then saved or stored for future use
in communications as illustrated by block 405. Like the method
illustrated in FIG. 3, the method illustrated in FIG. 4 may be
employed on a limited basis, such as only during time periods when
no signal of interest is present, for example, or may be employed
continuously. In other words, the method specifically identified in
FIG. 4 may, like that method illustrated in FIG. 3, be employed in
a continuous loop type fashion, for a limited or extended period of
time. In either case, the error power of the input is estimated
over time, fidelity metrics are determined (each using one or more
error power estimates of the input) and information about the
fidelity metrics stored for future use in communications.
[0068] Specifically, the stored information about the fidelity
metrics may be used in transmit waveform optimization for example.
In other words, the information may be used to determine a waveform
that best suits the communication channel given what has been
learned about the channel over time, as reflected in the stored
fidelity metrics. The stored information about the fidelity metrics
may also (or alternatively) be used in selecting receiver
algorithms that are robust given the limitations of the channel,
again as reflected in the stored fidelity metrics. Additional
detail regarding use of catalogued channel fidelity metric
information for future communications is provided below.
[0069] In one embodiment of the present invention, the methods
discussed above with respect to FIGS. 3 and 4 may be used in
conjunction with each other. For example, the method of FIG. 3 may
be employed when a signal of interest is present, while the method
of FIG. 4 may be used when a signal of interest is not present.
[0070] The error power estimates provided previously with respect
to FIGS. 2 through 4 may be generated in a number of ways, in the
presence or absence of a signal of interest. In the absence of a
signal of interest, the input power to the receiver may simply be
the noise power. Filtering to the bandwidth of the signal of
interest may be used if desirable.
[0071] In an embodiment where the communication system (similar to
the system of FIG. 2) is a digital communications system, one
particular method for gathering the error power estimates during
signaling is to calculate the distance (squared, for power) between
the received signal and the nearest constellation point in the
digital system's signaling alphabet. This error vector is typically
available or readily obtainable from a slicer in a digital
communications receiver.
[0072] The length of sliding window 205 of FIG. 2 is important in
its selection and application, but in general, may be any length. A
shorter window is a subset of a longer window, so longer and longer
windows may theoretically provide better and better channel
fidelity metrics. However, in practice, the window length should,
for example: (1) be sized to accommodate a given or tolerable
amount of delay (acceptable to the rest of the receiver processing)
in generating the channel fidelity metric; (2) not unduly increase
the complexity of the overall receiver; and (3) account for the
durations or dynamics expected, or previously observed, in the
dominating channel impairments. For example, if transitory channel
impairment has duration of up to 10 symbols in a given digital
communications system, then it is hard to justify the use of a
window of 100 symbols. Similarly, a window of only 4 symbols, with
the expectation of a persistence of 10 symbols of a given
impairment condition, needlessly lessens the ability of the
fidelity processor to make the best channel fidelity assessment, as
it is denied relevant or correlated information regarding the
channel fidelity.
[0073] Many forms are contemplated for processing the sequence of
error power estimates in the fidelity processor 207 of FIG. 2. Such
forms may depend on: (1) the complexity allowed; (2) the size of
the sliding window or duration or persistence of the impairment
states; (3) the delay allowed in generating the channel fidelity
metrics; and (4) the use of the channel fidelity metric (i.e., the
accuracy of the metric in matching the impairment level).
[0074] In its most simple form, the fidelity processor 207 may
simply compare each error power estimate against a threshold, and
output a binary channel fidelity estimate--i.e., "channel OK," and
"channel degraded." While the window in this case consists of a
single sample (or a single symbol in the digital communications
example), the use of the catalogue of this information, and the
beneficial use of this metric in subsequent receiver processing,
may be employed in one embodiment of the present invention (such as
shown in FIG. 2 and discussed with respect to FIGS. 3 and 4, for
example).
[0075] FIG. 5A illustrates a flow diagram of a method of impairment
mitigation in accordance with one specific embodiment of the
present invention, for use in connection with digital
communications. First, a set of symbols is received as illustrated
by block 501A, and the closest constellation point to each of the
symbols is determined as illustrated by block 503A (i.e., multiple
symbols each having a corresponding closest constellation point).
As provided previously, the closest constellation point may be
determined from a slicer in the receiver.
[0076] Next, the error power of the symbols is calculated using,
for example, the square of the distance between the received signal
and the nearest constellation point in the digital system's
signaling alphabet, also as provided previously and as illustrated
by block 505A. The sum of the error power of all the symbols is
then compared to a threshold of error power as illustrated by block
507A. This is performed, for example, in the fidelity processor. If
it is determined that the error power is greater than the threshold
(i.e., the error power is too large), it is assumed that the
channel is degraded, and all the symbols (i.e., the entire set) are
erased (or weighted low likelihood) as illustrated by block 509A.
If instead it is determined that the calculated error power of the
symbols is not above the threshold (i.e., less than the threshold),
it is assumed that the channel is OK, and the symbols are kept as
illustrated by block 511A. In either case, the decision is
communicated to the decoder as illustrated by block 513A. In other
words, if the symbols are kept as illustrated by block 511A, the
symbols are simply communicated to the decoder as illustrated by
block 513A, whereas if the symbol is erased as illustrated by block
509A, an indication that the symbols have been erased is
communicated to the decoder as illustrated by block 513A. This
process is repeated for each set of symbols received.
[0077] While the method illustrated in FIG. 5A is performed on
multiple symbols in a set, it is contemplated that a subset of
symbols may be considered. Furthermore, it is contemplated that one
or more chip(s) (or other waveforms) may play the roll of a
symbol.
[0078] For example, with SCDMA, after hard decisions are made in an
iterative process or manner, these hard decisions may be
remodulated and the error power calculated for each chip in the
spreading interval. FIG. 5B illustrates a flow diagram of a method
of impairment mitigation in accordance with one specific embodiment
of the present invention. First, all 128 chips in a set are
received as illustrated by block 501B, and the closest
constellation point to all the chips is determined as illustrated
by block 503B. As provided previously, the closest constellation
point may be determined from a slicer in the receiver. The hard
decisions are remodulated as illustrated by block 504B. Next, the
error power of the chips is calculated as illustrated by block
505B. It is contemplated that blocks 503B and 505B calculate the
error power of a chip using the remodulated ensemble set of symbol
hard decisions (i.e., the closest constellation point for each of
the demodulated spread symbols). The error power of each chip is
then compared to a threshold of error power as illustrated by block
507B. If it is determined that the error power is greater than the
threshold (i.e., too large), it is assumed that the channel is
degraded, and the chips are erased (or weighted low likelihood) as
illustrated by block 509B. If instead it is determined that the
calculated error power of the chip is not above the threshold, it
is assumed that the channel is OK, and the chips are kept as
illustrated by block 511B. In either case, the decision is
communicated to the decoder as illustrated by block 513B or
repeated for multiple iterations. Moreover, the methods provided
previously may be employed using different means for calculating
the error power and different processing may be used to determine
whether or not the channel is OK or whether particular symbol(s)
(chips or other waveforms) should be erased or kept. In addition,
the method may be employed in connection with analog
communications, using samples rather than symbols. As mentioned
above with respect to FIG. 4, channel fidelity metric information
obtained from the fidelity processor may be stored and used for
future communications. In the particular example illustrated in
FIGS. 5A and 5B, by analyzing the duty factor of the "channel OK"
versus the "channel degraded" condition, and by analyzing the
relative persistence of these conditions, the transmitting waveform
may be adapted to these parameters. The appropriate amount of
parity in FEC coding, and the best choice of interleaver parameters
in FEC employing interleaving, are strongly related to these
parameters.
[0079] Similarly, as provided previously with respect to FIG. 3,
the receiver may make use of this information directly. In the
example of digital communications, the receiver marks the bits
corresponding to the "channel degraded" condition as having very
low confidence in subsequent FEC decoding. Reed-Solomon codes may
accommodate both error correction and erasure marking in their
decoding. By marking Reed-Solomon symbols that contain bits
transmitted during "channel degraded" conditions as erasures, the
decoder benefits from having more information than a typical
Reed-Solomon decoder working with hard decisions only. In other
words, using the side information about the channel fidelity, the
decoder may produce better results (i.e., higher rate of correct
decoding).
[0080] A Reed-Solomon decoder may accommodate twice as many erased
symbols as it can correct erred symbols, so finding instances of
degraded channel fidelity, which often lead to erred Reed-Solomon
symbols benefits the decoder, and marking these as erasures,
greatly benefits the decoder. If nearly all of the Reed-Solomon
symbol errors are attributable to the degraded channel, and if the
degraded channel is fairly accurately detected (in the fidelity
processor for example), then almost twice as many instances of the
degraded condition may be tolerated.
[0081] Other examples of fidelity processors include summing the
error power estimates in the sliding window, and providing these
(or a scaled version such as an average) as the channel fidelity
estimate. Alternately, this sum or average may itself be quantized
into a binary decision, or a finite number of levels (such as
"channel pristine," "channel OK," and "channel degraded" in one
example), or even compressed, via a square root operation, for
example. If a dominant channel impairment is expected to persist
for a duration of many symbols, then summing the error power
estimates for at least several symbols increases the accuracy of
the channel fidelity metric, especially during the "middle" of the
impairment condition.
[0082] However, determining the precise moment when the degraded
condition is "turned on" and "turned off" may be difficult if a
long window for summing is used, without modification. One approach
is to increase the time-domain precision of the fidelity processor,
to compute the average error power during a window, and apply two
thresholds, one on the average and one on individual samples of the
error power estimates. The "channel degraded" assignment is only
outputted at times corresponding to samples, where the average
error power in the window exceeded threshold #1, AND either (a) the
sample was between two samples which exceeded threshold #2, or (b)
the sample was the only sample in the window which exceeded
threshold #2.
[0083] Once again, a particular example may be employed to the
summing of the noise power estimates within the window, as provided
previously, and this result compared with a threshold. This binary
channel fidelity metric is then associated with the middle sample
of the window (i.e., where the delay corresponds to half the window
duration). With Reed-Solomon FEC, as provided previously, the
"channel degraded" associated with any bits in a Reed-Solomon
symbol result in that symbol being marked for erasure in the
decoding process. The method described above may again be applied
to enhance the time-domain precision of the channel fidelity
metric.
[0084] FIG. 6 illustrates a block diagram of an impairment
mitigation system 600 in accordance with a particular embodiment of
the present invention. The system 600 (similar to system 200 of
FIG. 2) may be contained, for example, in one or both of the
communication nodes of FIG. 1. Referring to FIG. 6, a receiver 601
receives at input 603 an input signal and/or noise, in addition to
a temporary high-level noise burst, for example. The receiver,
using slicer 605 and block 607, generates error power estimates.
The receiver generates such error power estimates either using a
sequence of bits (using a sequence of symbols is a set, in a
digital communications example). A sliding window 609, depicted as
a 7-tap delay line, receives the error power estimates, which are
then processed in a fidelity processor 611. The fidelity processor
611 continuously generates a metric for channel fidelity as the
window (i.e., time) progresses.
[0085] Specifically, 7-tap delay line 609 captures 7 consecutive
error power estimates at a time, and computes an average error
power using the 7 captured estimates. In addition, the highest
(maximum) error power of the first 4 captured estimates is
determined (i.e., estimates 1 through 4), and the highest (maximum)
error power of the last 4 captured estimates (i.e., estimates 4
through 7) is likewise determined. Next, a determination is made
whether the average error power calculated is greater than a first
threshold, and whether both maximum error powers are greater than a
second threshold. If any one is not above its respective threshold,
a "channel OK" indication is sent to the receiver 601. If all three
are above or greater than their respective thresholds, then a
"channel degraded" indication is sent to the receiver 601. This
indication may be a simple 1-bit channel fidelity metric (e.g., a
"1" for channel OK and a "0" for channel degraded for example). In
a digital communications example, the fidelity processor 611
generates a 1-bit channel fidelity metric over time for QAM
constellations, for example.
[0086] The receiver 601 receives the channel fidelity metric as
illustrated by reference numeral 613 and is aware of the 4 sample
or symbol delay as illustrated by reference numeral 615. Processing
block 617, knowing the channel fidelity metric and the particular
set of samples or symbols being considered from the known delay,
either erases the particular set of samples or symbols being
considered (corresponding to a "channel degraded" fidelity metric),
or keeps the particular sample or symbol being considered
(corresponding to a "channel OK" fidelity metric). This process is
repeated so that the error power estimate corresponding to each set
of samples or symbols is considered by the fidelity processor 611.
In the embodiment illustrated in FIG. 6, the particular set of
samples or symbols being considered by the fidelity processor 611
is that corresponding to the error power estimate found at the
4.sup.th position in the 7 tap delay line 609 (and hence the 4
sample or symbol delay).
[0087] A decoder 619, such as, for example, a Reed-Solomon Decoder,
decodes the set of samples or symbols with erasures, as determined
by the fidelity processor 611. Many different types of algorithms
may be used in the fidelity processor to generate fidelity metrics.
Decoded data, an estimate of the transmitted signals for example,
is outputted at output 621 of the receiver 610. It is contemplated
that the functionality of processing block 617 may be part of the
decoder 619. It is also contemplated that means other than as shown
in, or specifically discussed with respect to, FIG. 6 may be used
to calculate error power and to generate the fidelity metric.
Further, quantities other than 7 may be used for the tap delay
line.
[0088] In addition, while FIG. 6 illustrates a system having some
components and functionality located outside of the receiver, it
should be understood that such system may have additional
components or functionality located within the receiver, or may in
fact be entirely contained within the receiver. In addition, it
should also be understood that the estimation of the error power
and the processing depicted as being performed within the receiver
of FIG. 6, may instead be performed outside of the receiver.
[0089] FIGS. 7A & 7B illustrate a flow diagram depicting a
method that may be employed using the system of FIG. 6, in a
digital communications embodiment of the present invention. A set
of symbols is received as illustrated in block 701, and the closest
constellation point to each symbol in the set is determined as
illustrated in block 703. The error power of each symbol in a set
is calculated, for example, using the square of the distance
between the received symbol and the nearest constellation point in
the digital system's signaling alphabet, as provided previously and
as illustrated in block 705. Of course, other methods of
calculating or estimating the error power of each symbol in the set
may be used.
[0090] Next, the error power of a set of symbols is captured as
illustrated in block 707, and an average power of the captured set
is calculated as illustrated in block 709. In addition, a maximum
error power from a first portion of the set is determined as
illustrated in block 711, and a maximum error power from a second
portion of the set is likewise determined as illustrated in block
713.
[0091] The first and second portions of the set each include a
common symbol that is the "middle" symbol of the whole set (i.e.,
the last symbol of the first portion and the first symbol of the
second portion for example). In other words, for a set of length n,
an odd number, the middle symbol may be defined by 1+(n-1)/2. It is
this number that defines the symbol that is being considered as
well as the symbol delay for decoding purposes. Again, as provided
previously with respect to FIG. 6, the set length may be 7, which
makes symbol 4 the symbol that is being considered, and defines the
decoder delay to be 4 symbols. It is contemplated that even numbers
may be used for window length, too, and the symbol (or sample)
under consideration need not be the one in the center of the
window. The use of an odd window length and center symbol (sample)
for which the channel fidelity is being estimated is provided only
as an example.
[0092] The average error power of the sequence is then compared to
a first threshold as illustrated in block 715. If the average is
not above the first threshold, the common symbol is kept as
illustrated in block 717, otherwise, the maximum error power of the
first portion of the set is compared to a second threshold as
illustrated in block 719. If that first maximum is not above the
second threshold as illustrated in block 719, the common symbol is
kept as illustrated in block 717, otherwise, the maximum error
power of the second portion of the set is compared to the second
threshold as illustrated in block 721. If that second maximum is
not above the first threshold, the common symbol is kept;
otherwise, the common symbol is erased. In any case, the decision
of whether to erase or keep the common symbol is communicated to
the decoder as illustrated in block 725. The process is then
repeated, so that each set received is at some point considered
(i.e., each received symbol is the common set for one iteration of
the process).
[0093] For a 16 QAM constellation for example having a
constellation RMS power of 3.162 (i.e., the square root of 10) and,
for example, a 7-symbol sequence, the first threshold may be 0, and
the second threshold may be 0.64, for example. Of course, the
second threshold may be set to 0, such that just the average error
power of the whole sequence is used.
[0094] While the decisions made by blocks 715, 719 and 721 of FIGS.
7A-7B are shown to be in a particular sequence, any order of those
decisions may be employed. In addition, those decisions may instead
be performed simultaneously, rather than sequentially, as shown in
FIGS. 8A-8B. Specifically, decision block 801 of FIG. 8A replaces
the decision blocks 715, 719 and 721 of FIGS. 7A-7B. A single
determination is made at block 801 of FIG. 8A, based on the three
comparisons, whether the common symbol should be erased or
kept.
[0095] Another specific example illustrating applying the channel
fidelity metric to enhance the receiver processing follows with the
summing of the error power over a sliding window. Especially with
high density constellations, and with an impairment of low power or
one such as gain compression, where the impairment likely does not
cause the received, distorted signal to fall outside the normal
signaling constellation, the fidelity processor may determine the
presence of the impairment, but a significant fraction of the error
power estimates may be rather small (since the received signal
falls close to one of the many wrong symbols). In such instances,
even using convolutional coding FEC and traditional Viterbi
decoding, the branch metrics in the decoding process are not the
most accurate reflection of the state of the channel fidelity when
they are simply the error power estimates or log of error power
estimates (for each symbol) from the slicer. Knowing that a
degraded channel condition existed even when a signal was received
"close" to a constellation point may be very beneficially used in
the decoding, especially when "channel interleaving" is performed
prior to the decoding, thus dispersing the impacted symbols.
[0096] FIG. 9 illustrates a flow diagram illustrating a method that
uses a fidelity metric to modify branch metrics in the decoding
process, in accordance with one embodiment of the present
invention. First, a set of symbols is received as illustrated in
block 901, error power estimates are estimated or determined as
illustrated in block 903, and channel fidelity metrics are
determined using the error power estimates as illustrated in block
905. This may be achieved using any means provided herein, for
example. In addition, branch metrics are created as illustrated in
block 907. For example, in a Viterbi decoder example, branch
metrics are created for the Viterbi decoder branches. (Scaled
logarithms of the error power are typically used). The Viterbi
branches are normally inversely related to the error power from
various constellation symbols, since the branch metrics represent
the likelihood of the branch transition.
[0097] Once such branch metrics are created as illustrated in block
907, the branch metrics are modified based on the channel fidelity
estimate as illustrated in block 909. For example, the branch
metric may be set to a low probability value if the channel
fidelity is determined to be poor. Finally, decoding (Viterbi
decoding for example) is performed using the modified branch metric
as illustrated in block 911. This overall process may then be
repeated.
[0098] As mentioned previously, various embodiments of the present
invention provide for a fidelity processor that examines a sliding
window of error power estimates to yield a channel fidelity metric.
While specific fidelity processing examples have been discussed
above, still other types of fidelity processing may be employed in
connection with the various embodiments of the present invention.
For example, median filters or other ranking devices may be used.
In a median filter, the middle ranked value within a window is
output. Once again, as above, this value could be output "as is,"
or quantized with various thresholds, perhaps into a single binary
output.
[0099] Other forms of nonlinear filtering may also serve as useful
fidelity processors. For example, the error power estimates may be
quantized to a binary level with a threshold, i.e., "1" for greater
than threshold and "0" for less than threshold, and these quantized
samples filtered or averaged. This would simplify the "averaging"
complexity, and a second threshold as described above may be
applied to enhance the precision of marking the "turn on" and "turn
off" of the severe impairments.
[0100] Still other types of fidelity processing may include, for
example: (1) summing; (2) ranking; (3) thresholding and summing;
(4) summing and twice thresholding (sum and individual points in
the window); (5) quantizing the error power estimates or otherwise
nonlinearly processing them (e.g., square root or log); (6)
averaging across the window and taking the maximum of the average
and (some factor multiplying) the middle error power estimate in
the window; (7) taking the maximum of the median ranked value in
the window and (some factor multiplying) the middle error power
estimate in the window; (8) nonlinearly processing the error power
estimates and averaging; and (9) quantizing the results from the
aforementioned operations and/or nonlinearly processing them.
[0101] Furthermore, the channel fidelity metric may be used to
analyze channel behavior. More specifically, the channel fidelity
metric may be used to analyze for example duration and fraction of
time of impaired conditions compared to unimpaired conditions,
especially for determining most suitable FEC and symbol rates and
constellation sizes, etc., for the dynamically varying channel. In
addition, the channel fidelity metric may be applied to the
receiver for beneficial use of processing signals received
contemporaneously with the channel fidelity estimate. Some examples
of using the channel fidelity metric to enhance receiver
performance include: (1) marking Reed-Solomon symbols for erasure
in a Reed-Solomon decoder capable of erasure and error correction
decoding, and (2) in convolutional coded FEC (or other
soft-decision decoders, such as Turbo decoding), affecting the
soft-decision metric for a symbol with this additional channel
fidelity metric.
[0102] This latter embodiment especially benefits from this
technique if channel interleaving is performed on the symbol
soft-decisions prior to the decoding. Various embodiments of the
present invention are especially effective at enhancing receiver
performance with temporary high-level impairment duration of
multiple symbols, and with high density signaling constellations,
as seen in these particular examples.
[0103] While the error power estimates provided previously above
have been generated outside of the decoding process, decoding may
be used to generate a potentially improved error power estimate. In
other words, another method for generating the error power estimate
includes actually performing a preliminary decoding (if FEC is
employed), or partial decoding, and performing a better estimate of
the transmitted waveform to more accurately estimate the error
power. Such an approach means that there may be delays in the
generation of the error power estimates, but often this is not a
constraint. A second-pass at the decoding, now with the benefit of
the channel fidelity metric (versus time) arising from the
first-pass error power estimates, provides enhanced performance in
the time-varying impairment scenario.
[0104] FIG. 10 illustrates a block diagram of an impairment
mitigation system 1000 that uses preliminary decoding in generating
error power estimates, in accordance with one embodiment of the
present invention. The system 1000 may be contained, for example,
in one or both of the communication nodes of FIG. 1. A receiver
1001 receives an input 1003 containing one or more sets of symbols,
each set comprising one or more symbols, and performs normal hard
and soft-decisions. The information is then FEC decoded in FEC
decoder 1005, and the information is then re-encoded by encoder
1007. The re-encoded information is then used along with the
original input at 1003, to generate an error estimate as
illustrated by reference numeral 1009, which in turn is used to
calculate an error power estimate as illustrated by reference
numeral 1011.
[0105] A fidelity processor 1013 uses the error power estimate to
generate a channel fidelity metric, such as provided previously.
FEC decoder 1015 uses the channel fidelity metric, along with the
original, delayed input to decode the input, and output decoded
data (i.e., an estimate of the transmitted signal) at output 1017.
It is contemplated that the FEC decoder 1005 and FEC decoder 1015
may be separate units or devices, or combined into a single
decoder.
[0106] FIG. 11 illustrates a flow diagram depicting one embodiment
of a method of impairment mitigation that may be employed using the
system of FIG. 10, for example. One or more sets of symbols are
received as illustrated by block 1101, are decoded as illustrated
by block 1103 and then encoded as illustrated by block 1105. The
error power of the symbols in the received set(s) of signals is
then estimated using the encoded symbols as illustrated by block
1107. A channel fidelity metric is then determined using the error
power estimates as illustrated by block 1109, and the symbols are
decoded using the channel fidelity metric determined as illustrated
by block 1111. If at block 1103 it is determined that particular
received symbols cannot be decoded and thus re-encoded, then those
particular set of symbols are simply erased for estimation of
error, for example.
[0107] As may be understood upon reviewing FIGS. 10 and 11, the
system of FIG. 10 and method of FIG. 11 determine a fidelity metric
after an initial decoding, and use it to perform a subsequent
decoding. Multiple iterations of this process may be beneficial in
some embodiments.
[0108] Based on the above, various embodiments of the present
invention provide means for characterizing the transitory nature of
the high-level impairments (i.e., to develop knowledge)
characterizing not just typical or even average levels of
impairment, but an understanding and characterization of the
dynamic behavior of the impairment. With this knowledge, it is
possible to facilitate improved communications in the channel,
either by adjusting the transmission signal design, or by altering
or adjusting the receiver processing, or both.
[0109] If the dynamic nature of the impairments is so rapid that it
transitions from benign to severe and back to benign again, faster
than the receiver can determine and communicate this degradation
back to the original transmitter in the channel, then any
adjustments in the transmission waveform are "permanent," in the
sense that adaptation to the temporarily degraded channel is
precluded by the dynamics. Still, the optimal transmission waveform
may be different if and when it is learned that the channel
contains some severe but transitory impairment(s). Thus, it
benefits the communications system to learn and characterize the
transitory nature of the impairments, leading to a superior
transmit waveform with this new knowledge.
[0110] While some situations may preclude the feedback and
adjustment of the transmit waveform for adapting to a temporary
increase of an impairment, in such situations, the receiver may
still benefit from this knowledge.
[0111] Another embodiment of the present invention relates to the
detection and mitigation of temporary impairments in information
transfer between a plurality of CMs and/or CMTS. More specifically,
one embodiment of the present invention relates to some kind of
distortion inducing mechanism of one up to several chips (16 chips
for example), which introduces a brief, temporary distortion during
the operation of a communication system similar to that described
previously. This embodiment uses 128 chips to send a multiplicity
of symbols simultaneously during a fraction of that frame. If the
communication is subjected to a brief, temporary distortion, noise
for example, the suspect (disturbed) chips are identified and
blanked.
[0112] However, blanking the impacted chips may affect the
orthogonal spreading codes, such that they are no longer
orthogonal. For example, if six chips are blanked out, then
processing the remaining 122 of the 128 chips does not result in a
zero value (perfect orthogonality) for one spreading code passing
through another spreading code's matched filter (wipeoff), unless
the six blanked chips included three agreements and three
disagreements between the two codes (an unlikely occurrence). More
likely, there are an unequal amount of agreements and disagreements
between the two codes in the blanked chip, which then results in
ICI. In other words, a remnant of one code's symbol shows up at
another code's matched filter output.
[0113] FIG. 12 illustrates a high level flow chart depicting a
method for detecting a set of impacted chips or symbols in
accordance with one embodiment of the present invention. The
illustrated method includes making a preliminary decision on at
least one set of one up to 128 chips, symbols or waveforms
transmitted in one set as illustrated by block 1210. These chips in
the set are then remodulated, in one embodiment using spreading
codes, as illustrated by block 1212. Then, a determination of the
distortion or noise may be made at each chip position, between the
remodulated waveform and the received waveform as illustrated by
block 1214. One or more of the techniques provided previously may
be applied to this distortion embodiment. In one embodiment, these
techniques may be applied repeatedly. In other words, this process
may be repeated for multiple iterations.
[0114] FIGS. 13A & 13B illustrate a detailed flow chart
depicting an alternate method for detecting impacted signals in a
set (similar to that illustrated in FIG. 12) in accordance with one
embodiment of the present invention. This method illustrated in
FIG. 13 includes making a preliminary decision on each signal (for
example, chips, symbols, waveforms, etc.) transmitted in at least
one set as illustrated by block 1310. Such preliminary decisions
may include blanking the suspect chips using burst noise detection
as provided above or may include using raw received signal.
[0115] These signals are remodulated using spreading codes as
illustrated by block 1312. Remodulating the signals may include
putting the signals through a plurality or bank of mass filters and
producing a parallel set of slicer inputs. A determination
regarding the distortion or noise is made between the remodulated
signal and the received signal regarding each signal as illustrated
by block 1314. Such determination may include taking the missing
signals, wiping them off and recreating them, and determining a
likelihood ratio. Further, one or more of the techniques provided
previously and as set forth in the Detection and Mitigation and the
Blanking and Processing application, may be reapplied to such
distortion estimate. These processes may be repeated for multiple
iterations. At the end of one or more passes or iterations, a set
of signals may be identified as containing or being suspected of
containing elevated amounts of distortion and/or noise as
illustrated by block 1316.
[0116] The illustrated method continues, blanking the samples that
represent the identified suspect signals as illustrated by block
1318. In this embodiment blanking means are used to set the
received signal to a value of zero for the identified suspect
signal. The signals are correlated with the spreading codes as
illustrated by block 1320, providing a parallel set of inputs to
one or more slicers for each of the symbols in the set (e.g.,
spreading frame, 128 symbols) as illustrated by block 1322.
[0117] A hard-decision is made on each signal in the set using at
least one but up to 128 of the slicer inputs as illustrated by
block 1324. Such hard-decisions are then used in a Partial
Remodulator (alternatively referred to as "PRM") wherein the
composite transmitted value is reconstructed for each of the
blanked signals, forming Partially Remodulated Energy
(alternatively referred to as "PRME") as illustrated by block 1326.
In one embodiment, this assumes that such hard-decisions are the
actual transmitted signals. The PRME is correlated with the blanked
positions or signals for each of the spreading codes, and at least
one set of at least one but up to 128 partial correlation results
(alternatively referred to as "Partial Correlation Results" or
"PRCs") are added to the previously obtained blanked slicer inputs
as illustrated by block 1328. This new set of at least one but up
to 128 slicer inputs (alternatively referred to as "First Iteration
Blanking-Repaired" or "1st IBR" slicer inputs), is then
"hard-decisioned." This blanking-repaired set of decisions may be
used as the receiver hard-decisions. However, in another embodiment
the process may continue with at least one more iteration.
[0118] In a second iteration, the hard-decisions from the 1st IBR
slicer inputs are used in the PRM and the resulting PRME is
correlated with the blanked positions or signals. This set of at
least one but up to 128 partial correlation results (alternatively
referred to as "2nd PRCs") is added to the blanked slicer inputs.
In one embodiment, the 2nd PRCs are added to the original blanked
slicer inputs. This addition results in at least one set of one but
up to 128 new slicer inputs (alternatively referred to as the "2nd
IBR slicer inputs"). These 2nd IBR slicer inputs are then
"hard-decisioned." These latest hard-decisions may be the final
hard-decisions, or another iteration may be performed.
[0119] In subsequent iterations, the hard-decisions from the Nth
IBR slicer inputs are again passed to the PRM, and the resulting
PRME is correlated with the blanked signals or chip positions,
yielding N+1st PRCs. In one embodiment, the N+1st PRCs are added to
the original blanked slicer inputs, producing N+1st IBR slicer
inputs. Such N+1st IBR slicer inputs may again be
"hard-decisioned". This blanking-repaired set of decisions may
again be used as the receiver hard-decisions.
[0120] The process may, in one embodiment, continue for a specific
number of iterations (one or two iterations for example) or end
when stability is reached. In one or more embodiments of the
present invention, stability is defined as reaching the same
hard-decisions in two successive iterations, however other ending
criteria are contemplated. It is also possible to execute one or
more of the previously described iterations and use such
hard-decisions as feedback to a burst noise estimator (not shown).
Such burst noise estimator may be used to refine the estimate of
which set of signals or chips were indeed impacted by increased
distortion/noise. Then the iterative processing may start anew,
with one or more sets of new blanked signals, chip positions or
waveforms.
[0121] As provided previously, one embodiment of the present
invention relates to a mitigation process using orthogonal
decomposition of individual chips, signals, waveforms, etc. It is
contemplated that this invention may be applicable to any set of
orthogonal waveforms spanning the time-frequency signal space. In
such embodiments, the received waveform is decomposed into the
various components of the new basis set. Any distorted or noisy
dimensions are estimated (similar to that provided previously using
a "dimension component" replacing the chips for example). These
distorted/noisy dimensions are blanked, and the time-domain
waveforms regenerated but without such distorted/noisy dimensions.
As an example, a combination of frequency domain interference and
time domain burst noise could be mitigated with a concatenation of
a frequency domain notched filter (e.g., a conventional filter) and
the time domain sample blanking previously described in detail. The
subsequent time-domain waveforms are correlated with each of the
spreading codes, providing the blanked slicer inputs. The PRM takes
the hard-decisions using such blanked slicer inputs, computing the
PRME needed to be reintroduced due to the elimination of some of
the signal space dimensions. The iterations continue just as above,
except in this embodiment the transfer from chip samples to the
desired basis set and back again must be included in the
processing.
[0122] One embodiment of the present invention comprises one or
more sets of symbols, signals, waveforms, etc., each set comprising
one or more symbols, signals, waveforms, etc. having temporary
high-level impairments of a duration of one up to several chips.
Means are provided for detecting such temporary high-level
impairments) in the symbols, and then mitigating such temporary
large impairments to improve the reliability of the symbol
decisions even in the presence of such impairments.
[0123] It is contemplated that the SCDMA spreading codes may no
longer be orthogonal, owing to such temporary high-level
impairment. One embodiment of the present invention relates to
mitigating, if not eliminating, such lack of orthogonality. This
embodiment further relates to determining final hard-decision
estimates for the transmitted symbols, and using such estimates to
reduce such ICI.
[0124] A plurality of means for detecting symbols with increased
noise or distortion in single-carrier carrier modulations are
described previously. Such modulation means for detecting and
mitigating such temporary high-level impairments may include using
SCDMA modulation to detect increased noise/distortion in the
symbols when multiple symbols are transmitted simultaneously.
Furthermore, such modulation means may include OFDM, DMT and DWMT
techniques for example using different but contemporaneous
dimensions in the time-frequency domain.
[0125] It is further contemplated that transmitting and receiving
signals using such concurrent symbol-modulation, like the
embodiments provided above, may be used to: (1) catalog the
severity, duration, repetitiveness, and perhaps other features of
such impairment bursts; and (2) attempt to mitigate the impact of
the increased impairment level using the identification of this
condition in modified receiver processing. Such mitigation includes
enabling erasure decoding when Reed-Solomon FEC is transmitted, and
adjusting the soft-decision metrics to account for the low quality
of the symbol estimates during the recognized bursts of large
impairment, when Viterbi decoding (or any other soft decoding
algorithm) is employed.
[0126] In one embodiment of the present invention using concurrent
symbol-modulation (similar to the embodiments discussed previously
using single-carrier modulation), one or more matched filter(s) may
be used to process the received waveform, such that a soft-decision
value is determined for each modulated symbol in a set of symbols.
This value is inputted to a slicer. The slicer determines the
closest constellation symbols for each soft-decisioned input. This
closest constellation point represents the initial hard-decision
estimate for the respective transmitted symbols.
[0127] In the previous disclosed embodiments using single-carrier
modulation, the difference between the slicer inputs (i.e., the
soft-decision values) and the hard-decision estimate (i.e., the
selected closest constellation point) is the error vector. The
magnitude or power of such error vectors are processed to determine
which symbols are suspected of having been impacted by increased
impairment levels. However it is contemplated that the impairment
levels may rise and return to normal again in almost any amount of
time. In fact, the "typical" duration is in general unknown, and
must be determined or learned to enhance the accuracy of the
identification of the disturbed or distorted symbols. A form of
Heisenberg's uncertainty principle develops, wherein a great deal
of smoothing (a wide or long observation window in the time domain
covering many symbols) is used to estimate burst disturbances by
lessening the precision in determining where the increased
disturbance began and ended. Non-linear processing techniques may
be used as provided previously to address this issue, but the
fundamental issue still exists nonetheless.
[0128] In one embodiment of the present invention using concurrent
symbol-modulation, however, one or more sets comprising a plurality
of symbols are transmitted simultaneously, so that generally more
than one and up to 128 error vectors are obtained at one time. The
present invention contemplates handling up to 128 error vectors in
parallel, summing the error vector power over the full set of
symbols of each spreading frame. Adjusting the thresholds, either
manually or using some sort of automatically self-adjusting
thresholds, to compare to the total error vector power yields a
much more sensitive and accurate means for determining when a burst
of elevated disturbances has occurred. It is analogous to knowing
that a particular set of 128 symbols are impacted in the
single-carrier case, and being able to make a robust determination
of channel quality (similar to an SNR estimate for example) using a
window focused on more than one and up to 128 symbols known to
share the same environment.
[0129] Thus, using SCDMA modulation, or any other modulation
techniques using multiple symbols at the same time (multitone for
example), a set of up to 128 symbols are obtained. Each symbol may
be only slightly or greatly degraded, but all such symbols are
impacted similarly by the intermittent channel impairments. This is
in contrast to the TDMA techniques discussed previously, where only
one or a few symbols may be impacted.
[0130] In one or more embodiments using concurrent
symbol-modulation, where only one or a handful of chips (16 for
example) are impacted by the impairment, the technique of examining
the decision error power over the full set of 128 symbols, either
using averaging or other estimation techniques (some of which are
provided previously), may not yield a decisive result. However, the
remodulation technique discussed previously will likely identify
the set of chips where the distortion is isolated in such cases,
and chip blanking or likelihood ratio technique may be used to
mitigate such impairment.
[0131] FIG. 14 illustrates a block diagram of one embodiment of an
SCDMA receiver according to the present invention. It is
contemplated that such SCDMA receiver may be used in any of the
channels or systems discussed previously. The SCDMA receiver,
generally designated 1600, is adapted to detect and mitigate
temporary impairments or bursts in a channel using concurrent
symbol-modulating techniques. It is contemplated that the
illustrated SCDMA receiver 1600 may be used with at least one set
or sets comprising multiple symbols transmitted at the same time,
examining one or more frames (i.e., adjacent frames). In one
embodiment using 128 spreading codes, 128 symbols may be
transferred to the receiver 1600 at the same time in the same
set.
[0132] In this embodiment, the receiver 1600 comprises a
demodulator 1610 adapted to receive and demodulate one or more sets
comprising a plurality of waveforms, chips, symbols, signals, etc.
The demodulator 1610 is communicatively coupled to one or more
matched filters 1612 (for 128 spreading codes for example), adapted
to output one or more Y matched filter outputs (alternatively
referred to as "soft-decision values," Y.sub.1 through Y.sub.128
for example). In one embodiment of the present invention, the
matched filters 1612 are adapted to isolate each demodulated
transmitted symbol from all the other symbols in the set.
[0133] In single-carrier modulations as provided previously, a
matched filter processes the received waveform, and a soft-decision
value is determined for each modulated symbol. The soft-decisioned
values are input to at least one or more slicers 1614. Using SCDMA
modulation, it is contemplated that a bank of matched filters 1612,
each uniquely processing the received waveforms in each set,
produces a parallel set of soft-decisioned values at the end of
each spreading interval. More specifically, it is contemplated that
the filters 1612 output a vector of 128 matched filter outputs or
soft-decisioned values. In this embodiment, it is contemplated that
such unique processing comprises one matched filter and
soft-decisioned value per input symbol. The slicer 1614 determines
the closest constellation symbol for each received soft-decisioned
value input (Y.sub.1 through Y.sub.128 for example). This closest
constellation point represents the initial hard-decision estimate
(S.sub.1 through S.sub.128 for example) for each transmitted
symbol.
[0134] The matched filters 1612 and slicer 1614 are illustrated
communicatively coupled to one or more error vector calculators
1616, which is adapted to receive such Y soft-decision values and S
hard-decision estimates. In the illustrated embodiment, the
calculator 1616 is adapted to receive a plurality of Y matched
filter outputs or soft-decision values (Y.sub.1 through Y.sub.128
for example) and a plurality of hard-decision estimates ( S.sub.1
through S.sub.128 for example).
[0135] In one embodiment, the calculator 1616 is adapted to
calculate or determine a difference between each hard-decisioned
estimate and each associated soft-decision value, generating one or
more estimate values e. In one embodiment, the calculator 1616
yields a vector of one or more estimate vectors ( .sub.1 through
e.sub.128 for example) for the full set of symbols of a set. The
calculator 1616 produces a plurality of error vectors (128 for
example) at one time. All the error vectors are similarly affected
by such temporary impairment.
[0136] The error vectors are processed to determine what symbols
are suspected of being disturbed. Channel impaired decision block
1618 is illustrated communicatively coupled to the error vector
calculator 1616, and is adapted to receive the at least one
estimate vector or value ( e.sub.1 up to about .sub.128 for
example). The block 1618 is adapted to determine what symbols are
suspect based on the magnitude of error vectors. Block 1616 makes a
decision on such error vectors or values using averaging or other
estimation techniques.
[0137] Block 1616 is illustrated coupled to erasure block 1620,
which also receives inputs from slicer 1614. In one embodiment, the
erasure block 1620 is similar to a Reed-Solomon Decoder block as
provided previously. In this embodiment, the block 1620, either
erases or keeps the particular sample or symbol being considered.
This process may be repeated so that the error power value or
estimate corresponding to each sample or symbol is considered by
block 1620. The block 1620 then decodes the samples or symbols with
erasures. Many different types of algorithms may be used to decode
such samples or symbols. Decoded data (a final hard-decision
estimate of the transmitted signal), is outputted. In one
embodiment, 128 hard-decisioned values ( S.sub.1 through S.sub.128
for example) are outputted. In another embodiment, it is
contemplated that a likelihood ratio may be determined for each
symbol in the set.
[0138] FIG. 15 illustrates a flow diagram illustrating one
embodiment of a method, generally designated 1700, for detecting
and mitigating temporary impairments of one up to several chips in
a set using a receiver (a SCDMA receiver for example) similar to
that provided previously. This method comprises detecting such
temporarily higher amounts of impairment in the symbols and
improving the reliability of the symbol decisions even in the
presence of such impacted or distorted chips. The method comprises
demodulating the at least one modulated transmitted symbols or
signals in each set of symbols or signals using one or more
spreading codes as illustrated by block 1710. In one embodiment, up
to 128 symbols are demodulated.
[0139] The method further comprises determining soft-decisioned
values for the demodulated symbols as illustrated by block 1712. In
one embodiment, one or more matched filters are used to produce one
or more Y soft-decision values or matched filter outputs (Y.sub.1
through Y.sub.128 for example).
[0140] In one embodiment of the present invention, SCDMA
modulations (similar to the previously discussed single-carrier
modulations) one or more matched filters may be used to process the
received waveform, such that a soft-decision value is determined
for each modulated symbol. This value is inputted to a slicer. The
slicer determines the closest constellation symbol for each soft
input. This closest constellation point represents the initial
hard-decision estimate for the respective transmitted symbol as
illustrated by block 1714.
[0141] One or more error vectors are determined as illustrated by
block 1716. In the previous disclosed embodiment using
single-carrier modulations, the difference between the slicer
inputs (i.e., the soft-decision values) and the hard-decision
estimates (i.e., the selected closest constellation point) is the
error vector. The magnitude or power of such error vectors are
processed to determine which symbols are suspected of having been
impacted by increased impairment levels.
[0142] In one embodiment of the present invention using SCDMA
modulation, a plurality of symbols are transmitted simultaneously,
so that generally more than one and up to 128 error vectors are
obtained at one time as illustrated. The present invention
contemplates handling up to 128 error vectors in parallel, summing
the error vector power over the full set of symbols of each set.
Adjusting the thresholds, either manually or using some sort of
automatically self-adjusting thresholds, to compare to the total
error vector power yields a much more sensitive and accurate means
for determining when a burst of elevated disturbances has occurred.
It is analogous to knowing that a particular set of 128 symbols are
impacted in the single-carrier case, and thus being able to make a
very robust determination of channel quality (similar to an SNR
estimate for example) using a window focused on more than one and
up to 128 symbols known to share the same environment.
[0143] It is further contemplated that, in the single-carrier case,
the environment may change from symbol-to-symbol, and thus a large
window (such as 128 symbols for example) may not provide the
accuracy needed to determine which symbols are impacted. In SCDMA
modulation however, all the symbols in a set are impacted
similarly. It is contemplated that the spectral variations of the
codes may show 10 dB of variation from symbol-to-symbol in the same
set t in the presence of narrowband interference. However, such
special cases of narrowband interference are handled differently
from the rapidly time varying impairments targeted in the
embodiment of the present invention. The latter impact many of the
spreading codes at a time. The narrow band interference may be
rejected via notched filters applied after time-domain blanking or
the notched filtering may be performed prior to sample or chip
blanking, or even a combination of blanking before and after
filtering. With a combination of narrow band interference and
time-domain burst noise, one embodiment would blank impacted
samples (e.g., burst noise) prior to notched filtering and adapt
the coefficient of the notched filter in subsequent computations of
the filter output, in accordance with which input samples were
blanked. The notched filter coefficient will in general vary with
different combinations of blanked input samples. With different
blanking patterns, different coefficients are required to try to
attenuate the narrow band interference.
[0144] Thus, using SCDMA modulation, or any modulation techniques
using multiple symbols at the same time in the same set (multitone
for example), a set of up to 128 symbols are obtained, where all
such symbols are impacted by an intermittent channel impairment.
This is in contrast to the TDMA techniques discussed previously,
where only one or a few symbols may be impacted. Thus it is
contemplated that it may be easier to determine when the
intermittent impairment has significantly impacted the set of up to
128 symbols with accuracy. The statistics for determining the error
power arising from the slicer hard-decision values versus the
soft-decision values has, in this embodiment, the advantage of
viewing many "snapshots" or samples of error power (128 samples for
example) The ability of one embodiment of the present invention
using SCDMA modulation to more accurately detect the symbols
impacted by an intermittent impairment enables using erasure
decoding with greater benefit than that afforded in the TDMA case
with relatively short bursts of the impairment, since a decision on
the presence of the impairment must be made with little data,
relatively speaking, in the TDMA case.
[0145] A decision is made on the channel impairments as illustrated
by block 1718. In one or more embodiments using SCDMA modulation,
where only one or a handful of chips (16 for example) are impacted
by the impairment, the technique of examining the decision error
powers over the full set of 128 symbols, either using averaging or
other estimation techniques (some of which are provided
previously), may not yield a decisive result. However, the
remodulation technique discussed previously will likely identify
the small set of chips where the distortion is isolated in these
cases, and chip blanking rather than symbol erasing may be used to
mitigate such distortion.
[0146] The hard-decision values are determined as illustrated by
block 1720. In this embodiment, a hard-decision is made to keep or
erase a particular set of samples or symbols being considered. This
process may be repeated so that the error power estimate
corresponding to each sample or symbol is considered. Finally, the
sets of samples or symbols with erasures are decoded.
[0147] FIG. 16 illustrates a block diagram of an impairment
mitigation system 1800 in accordance with a particular embodiment
of the present invention. The system 1800 (similar to system 600 of
FIG. 6) may be contained, for example, in one or both of the
communication nodes discussed previously. Referring to FIG. 15,
receiver 1801 receives at input 1803 an input signal, in addition
to an occasional or temporary high-level impairments, for example.
In one embodiment, input 1803 comprises one or more sets, each set
comprising one or more symbols. The receiver, using-matched filter
1805, slicer 1806 and calculator 1807, generates error power
estimates. The receiver 1801 generates such error power using a set
of symbols in a digital communications example (or on a bit by bit
basis or using a sequence of bits, or sample-by-sample in an analog
waveform). A sliding window 1809, receives the error power
estimates (128 for example), which are then processed in a channel
impaired decision block 1811. The block 1811 continuously generates
a metric for channel fidelity as the window (i.e., time)
progresses.
[0148] Specifically, 1811 receives the 128 concurrent error power
estimates at a time, and computes an average error power using the
128 captured estimates. The decision block 1811 may average the
error vectors' powers or discard the extreme vectors (for example,
discarding the ten largest and ten smallest vectors) and average
the remaining error vectors' powers. In the illustrated embodiment,
the block 1811 computes the average. Next, a determination is made
whether the calculated average error power is greater than a
predetermined threshold (i.e., too large). If it is not above its
respective threshold, a "channel OK" indication is sent to the
receiver 1801. If it is above or greater than its respective
threshold, then a "channel degraded" indication is sent to the
receiver 1801. This indication may be a simple 1 bit channel
fidelity metric (e.g., a "1" for channel OK and a "0" for channel
degraded for example). In a digital communications example, the
fidelity processor 1811 generates a 1-bit channel fidelity metric
over time for QAM constellations, for example.
[0149] In the above example, one set of 128 concurrent symbols is
evaluated; in alternate embodiments, consecutive sets of concurrent
symbols may be examined or processed to identify a degraded channel
condition. This is the "sliding window" approach with the window
covering more than one concurrent set of symbols.
[0150] The receiver 1801 receives the channel fidelity metric as
illustrated by reference numeral 1813. Processing block 1817,
knowing the channel fidelity metric and the particular set of
samples or symbols being considered from the known delay, either
erases (weights with low likelihood) the particular set of samples
or symbols being considered (corresponding to a "channel degraded"
fidelity metric), or keeps the particular sample or symbol being
considered (corresponding to a "channel OK" fidelity metric). This
process is repeated so that the error power estimate corresponding
to each set of samples or symbols is considered by the block
1811.
[0151] A decoder 1819, such as, for example, a Reed-Solomon
Decoder, decodes the set of samples or symbols with erasures, as
determined by the block 1811. Many different types of algorithms
may be used in the fidelity processor to generate fidelity metrics.
Decoded data, an estimate of the transmitted signal for example, is
outputted at output 1821 of the receiver 1801. It is contemplated
that the functionality of processing block 1817 may be part of the
decoder 1819. It is also contemplated that means other than as
shown in, or specifically discussed with respect to FIG. 16 may be
used to generate the fidelity metric.
[0152] In addition, while FIG. 16 illustrates a system having some
components and functionality located outside of the receiver 1801,
it should be understood that such system may have additional
components or functionality located within the receiver 1801, or
may in fact be entirely contained within the receiver 1801. In
addition, it should also be understood that the estimation of the
error power and the processing depicted as being performed within
the receiver 1801 of FIG. 16, might instead be performed outside of
the receiver.
[0153] Many modifications and variations of the present invention
are possible in light of the above teachings. Thus, it is to be
understood that, within the scope of the appended claims, the
invention may be practiced otherwise than as described
hereinabove.
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