U.S. patent application number 13/442150 was filed with the patent office on 2012-09-06 for digital equalization process and mechanism.
This patent application is currently assigned to Adaptive Networks, Inc.. Invention is credited to Michael B. Propp, Khaled Saab.
Application Number | 20120224661 13/442150 |
Document ID | / |
Family ID | 23205310 |
Filed Date | 2012-09-06 |
United States Patent
Application |
20120224661 |
Kind Code |
A1 |
Propp; Michael B. ; et
al. |
September 6, 2012 |
Digital Equalization Process and Mechanism
Abstract
A filter is created by sampling noise during an inter-frame gap
of a received signal, sampling a data frame preamble from within a
data frame of the received signal, and computing filter
coefficients based on the noise sampled during the inter-frame gap
and the data frame preamble sampled from within the data frame.
Inventors: |
Propp; Michael B.;
(Brookline, MA) ; Saab; Khaled; (Randolph,
MA) |
Assignee: |
Adaptive Networks, Inc.
Needham
MA
|
Family ID: |
23205310 |
Appl. No.: |
13/442150 |
Filed: |
April 9, 2012 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10486243 |
Aug 4, 2004 |
8155176 |
|
|
PCT/US02/25138 |
Aug 9, 2002 |
|
|
|
13442150 |
|
|
|
|
60311081 |
Aug 10, 2001 |
|
|
|
Current U.S.
Class: |
375/346 |
Current CPC
Class: |
H03H 17/02 20130101;
H04B 3/145 20130101; H04L 2007/047 20130101; H03H 17/0294 20130101;
H04L 2025/03522 20130101; H03H 17/0213 20130101; H04B 2203/5491
20130101; H04B 3/54 20130101; H03H 21/0012 20130101; H04L 25/03159
20130101; H04B 2203/5425 20130101 |
Class at
Publication: |
375/346 |
International
Class: |
H04L 1/00 20060101
H04L001/00 |
Claims
1. A method for creating a filter, the method comprising: sampling
noise during an inter-frame gap of a received signal; sampling a
data frame preamble from within a data frame of the received
signal; and computing filter coefficients based on the noise
sampled during the inter-frame gap and the data frame preamble
sampled from within the data frame.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 10/486,243, filed Aug. 4, 2004, which is a .sctn.371 of
PCT/US02/25138, filed Aug. 9, 2002, which claims the benefit of
U.S. Provisional Application No. 60/311,081, filed Aug. 10, 2001,
and titled "Digital Equalization Process and Mechanism," which are
incorporated by reference in their entirety.
TECHNICAL FIELD
[0002] This disclosure relates to data communications over noisy
media with frequency-dependent attenuation.
BACKGROUND
[0003] In data communications, wideband transmission may be used.
However, the received signal may be impaired by noise and
frequency-dependent channel attenuation. For example, an entire
portion of the transmitted signal may fall into an attenuation null
and be severely attenuated. In addition, the intersymbol
interference (ISI) could degrade the signal causing a high bit
error rate which may render an error correction engine useless.
SUMMARY
[0004] In one general aspect, a filter is created by sampling noise
during an inter-frame gap of a received signal, sampling a data
frame preamble from within a data frame of the received signal, and
computing filter coefficients based on the noise sampled during the
inter-frame gap and the data frame preamble sampled from within the
data frame.
[0005] Implementations may include one or more of the following
features. For example, the filter may be structured and arranged to
filter received signals communicated across power lines.
[0006] The received signal may be synchronized to identify the data
frame preamble. The received signal may be synchronized by sampling
a randomly located portion of the received signal, generating a
pre-filter based on the randomly located portion of the sampled
received signal, applying the pre-filter to the received signal,
and identifying the data frame preamble based on the received
signal after applying the pre-filter.
[0007] The filter coefficients may be computed by generating noise
filter coefficients from the noise sampled during the inter-frame
gap, generating channel filter coefficients from the data frame
preamble sampled from within the data frame, and generating the
filter coefficients based on the noise filter coefficients and the
channel filter coefficients. The noise filter coefficients may be
generated from the noise sampled during the inter-frame gap and the
data frame preamble sampled from within the data frame.
[0008] The channel filter coefficients may be generated by
identifying channel filter coefficients based on a threshold
criteria and updating the channel filter coefficients that are
identified based on the threshold criteria. The channel filter
coefficients may be updated by performing an excision algorithm.
The channel filter coefficients may be updated by modifying the
channel filter coefficients. The channel filter coefficients may be
modified by reducing a magnitude of the channel filter coefficients
that are identified based on the threshold criteria.
[0009] Additionally or alternatively, the channel filter
coefficients may be updated by performing a smoothing algorithm.
The channel filter coefficients may be updated by replacing the
channel filter coefficients with replacement channel filter
coefficients. In one implementation, the replacement channel filter
coefficients may include channel filter coefficients not identified
based on the threshold criteria.
[0010] In another general aspect, adaptively filtering a data frame
of a received signal includes generating coefficients for an
adaptive filter based on at least noise from within an inter-frame
gap and a preamble of the data frame and filtering the data frame
by applying the adaptive filter to the data frame.
[0011] Implementations may includes one or more of the following
features. For example, the data frame may be communicated across
power lines such that the data frame may be filtered by applying
the adaptive filter to the data frame that is communicated across
the power lines.
[0012] The coefficients for the adaptive filter may be generated by
sampling noise during an inter-frame gap of a received signal,
sampling a data frame preamble from within a data frame of the
received signal, and computing filter coefficients based on the
noise sampled during the inter-frame gap and the data frame
preamble sampled from within the data frame.
[0013] The received signal may be synchronized to identify the data
frame preamble. The received signal may be synchronized by sampling
a randomly located portion of the received signal, generating a
pre-filter based on the randomly located portion of the sampled
received signal, applying the pre-filter to the received signal,
and identifying the data frame preamble based on the received
signal after applying the pre-filter.
[0014] The filter coefficients may be computed by generating noise
filter coefficients from the noise sampled during the inter-frame
gap, generating channel filter coefficients from the data frame
preamble sampled from within the data frame, and generating the
filter coefficients based on the noise filter coefficients and the
channel filter coefficients. The noise filter coefficients may be
generated from the noise sampled during the inter-frame gap and the
data frame preamble sampled from within the data frame.
[0015] The channel filter coefficients may be generated by
identifying channel filter coefficients based on a threshold
criteria and updating the channel filter coefficients that are
identified based on the threshold criteria. The channel filter
coefficients may be updated by performing an excision algorithm.
The channel filter coefficients may be updated by modifying the
channel filter coefficients. The channel filter coefficients may be
modified by reducing a magnitude of the channel filter coefficients
that are identified based on the threshold criteria.
[0016] Additionally or alternatively, the channel filter
coefficients may be updated by performing a smoothing algorithm.
The channel filter coefficients may be updated by replacing the
channel filter coefficients with replacement channel filter
coefficients. In one implementation, the replacement channel filter
coefficients may include channel filter coefficients not identified
based on the threshold criteria.
[0017] In another general aspect, a filter is derived from a
combination of coefficients. The coefficients used to derive the
filter include coefficients derived from noise sampled during a
period between data frames and a received data frame preamble and
coefficients derived from the received data frame preamble and a
transmitted data frame preamble.
[0018] Implementations may include one or more of the following
features. For example, the coefficients derived from the noise may
include an average of the coefficients derived from the noise over
a period of time. The coefficients derived from the received data
frame preamble may include an average of the coefficients derived
from the data frame preamble over a period of time. The filter
derived from the combination of the coefficients may include an
average of the filters derived from the combination of the
coefficients over a period of time. The coefficients derived from
the noise may include an average of the noise over a period of
time. The coefficients derived from the received data frame
preamble may include an average of the data frame preamble over a
period of time.
[0019] In another general aspect, receiving a signal transmitted
through at least first and second communication paths over a single
communication medium includes sampling the signal over the
communication paths to realize a first sampling from the first
communication path and a second sampling from the second
communication path, synchronizing the first sampling and the second
sampling, and combining the first sampling and the second sampling
to generate a signal representative of the signal transmitted
through the first and second communication paths.
[0020] Implementations may include one or more of the following
features. For example, the first sampling and the second sampling
may be synchronized by independently synchronizing the first
sampling and the second sampling and adjusting for a delay
difference between the first and the second communication paths
based on the independent synchronization of the first sampling and
the second sampling. The first communication path may include
line-neutral path. The second communication path may include a
neutral-ground path.
[0021] In another general aspect, pre-filtering a received signal
to improve synchronization includes sampling a randomly located
portion of the received signal, generating a pre-filter based on
the randomly located portion of the sampled received signal,
applying the pre-filter to the received signal, and identifying a
data frame preamble based on the received signal after applying the
pre-filter to improve synchronization.
[0022] Implementations may include one or more of the following
features. For example, sampling the randomly located portion of the
received signal may include sampling within noise and generating
the pre-filter may include generating the pre-filter based on the
sampled noise. The received signal may include data and noise. A
portion of the received signal may be sampled at a randomly
selected location.
[0023] In one implementation, the pre-filter may include a hybrid
prediction filter that produces a desired response as an inverse of
a value related to a portion of the received signal.
[0024] These general and specific aspects may be implemented using
a system, a method, or a computer program, or any combination of
systems, methods, or computer programs.
[0025] Other features will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0026] FIG. 1 is a block diagram of a data frame including time
intervals before and after the data frame.
[0027] FIG. 2 is a flow chart of an exemplary process for digital
equalization.
[0028] FIG. 3 is a flow chart of an exemplary process for
synchronizing an incoming bit stream.
[0029] FIG. 4 is a flow chart of an exemplary process for
constructing a frequency-domain equalization and noise filter.
[0030] FIG. 5 is a flow chart of an exemplary process for computing
a channel filter.
[0031] FIG. 6 is a block diagram of a system capable of performing
digital equalization.
[0032] FIG. 7 is a block diagram of a system capable of performing
data filtering.
[0033] FIG. 8 is a block diagram of a system capable of performing
pre-filtering to enhance synchronization.
[0034] FIG. 9 is a block diagram of a system capable of improving
reception of data frames under multipath propagation.
[0035] Like reference symbols in the various drawings may indicate
like elements.
DETAILED DESCRIPTION
[0036] FIG. 1 illustrates a portion 100 of an exemplary data frame
105 including time intervals before and after the frame, such as an
Inter-Frame Gap (IFG) 110. Data frame 105 may include bits
transmitted over a communications system, including a preamble 115
and a data portion 125. The preamble 115 may include a
synchronization preamble having a number of bits that are at the
beginning of each data frame 105. Additionally or alternatively,
preamble 115 may be located at the beginning of a data block, which
may include several data portions 125 arranged in series (now
shown). The preamble 115 may be used by a receiver to lock and to
synchronize with the bit timing of a transmitter. A received signal
may include any data, data gaps, and noise received, such as, for
example, data frame 105, IFG 110, noise 120, preamble 115, and data
125.
[0037] IFG 110 may include a gap between data frames 105 in which
data 125 typically is not transmitted. IFG 110 may include noise
120, e.g., resulting from a time interval with no transmitted
data.
[0038] In general, a network system that transmits and receives
data communications may detect the data frame 105 to perform
processes such as, for example, digital equalization and filtering.
For example, it may be desirable to construct noise filters and to
instantaneously adapt the equalization and noise filters on a
frame-by-frame basis. By instantaneously adapting the equalization
and the noise filters on a frame-by-frame basis, the need for
memory of the filter outside of the current data frame 105 and
dependency on the previous data frame 105 may be reduced or
eliminated altogether.
[0039] In one implementation, frequency-domain equalization may be
used to retrieve the transmitted data over a noisy transmission
channel with frequency-dependent attenuation (e.g., impairments of
white noise, periodic noise, multipath propagation, and different
impedances). A filter may be designed to reduce or eliminate the
noise, correct for the channel distortion and the channel
attenuation, and/or compensate for the ISI.
[0040] In this implementation, the desired equalization and
filtering may be accomplished by using the IFG 110 to sample the
noise, and then use the captured noise and the preamble 115 to
construct a filter (e.g., an optimal Wiener filter).
[0041] FIG. 2 illustrates a process 200 for digital equalization,
Process 200 typically includes synchronizing an incoming data frame
(step 210), constructing a frequency-domain equalization and noise
filter (step 220), and applying the filter to the frame from which
it was derived/constructed (step 230).
[0042] In general, synchronizing the incoming frame (step 210)
includes receiving a signal and locating a frame, namely,
identifying the beginning and/or the end of the frame. Various
modulation schemes may be used in transmitting and receiving bit
streams. In one example implementation, a binary phase shift keying
(BPSK) modulation scheme may be used, however, other modulation
schemes are also available for application to the digital
equalization process.
[0043] For synchronization (step 210), the incoming signal may be
over-sampled (e.g., by a factor of N) and an algorithm (e.g., a
maximal likelihood algorithm) may be used to estimate the incoming
bit value. The received bit sequence (e.g., a 64-bit preamble) then
may be correlated to the predefined and stored transmitted bit
sequence and submitted to decision logic that may determine if the
sequence corresponds to the predefined transmitted preamble. The
synchronization process 210 may be performed at the receiving
location of the incoming bit stream. For example, the
synchronization 210 may occur at a receiver circuit located at the
receiving location. Synchronization process 210 may be used to
maintain proper bit timing at the receiver location.
Synchronization process 210 may involve locating a sync position
(e.g., locating the preamble). By identifying the beginning and/or
the end of the frame in the synchronization (step 210), the IFG may
be located and noise within the IFG may be sampled in step 220.
[0044] Constructing the frequency-domain equalization and noise
filter (step 220) may include using one or more samples of the
noise in the IFG, the received preamble, and the transmitted
preamble. Once the incoming frame has been synchronized (step 210),
the noise may be sampled and one or more filters may be computed
and constructed. For instance, two complementary filters may be
computed (e.g., a noise filter and a channel filter). A combination
of the noise filter and the channel filter may be used to generate
a filter, such as a Wiener filter or some other filter capable of
being used as an optimal filter. Additionally and/or alternatively,
the construction of the filter (step 220) may include using an
average of samples of the noise in the IFG, the received preamble,
the transmitted preamble, and/or constructed filters over a period
of time.
[0045] Applying the filter to the frame from which it was derived
(step 230) may include multiplying the filter coefficients by the
incoming data frame. Examples of the processes described by steps
210 and 220 are described below in more detail with respect to
FIGS. 3 and 4-5, respectively.
[0046] FIG. 3 is an expansion of the synchronization process 210
from FIG. 2. FIG. 3 typically includes sampling the incoming signal
(step 310), estimating the incoming bit value (step 320), and
correlating the received bit sequence (e.g., the 64-bit preamble)
to a predefined bit sequence that was transmitted as the preamble
(step 330).
[0047] Over-sampling the incoming signal (step 310) may include
over-sampling the incoming signal by a factor of N. Over-sampling
of the signal (step 310) may be performed by an analog-to-digital
converter (ADC), or by another circuit capable of over-sampling an
incoming signal. Estimating the incoming bit value (step 320) may
include demodulating the over-sampled incoming data into a single
binary bit stream. Correlating the received bit sequence (step 330)
may include correlating the over-sampled incoming data stream
against a stored copy of the transmitted bit wave form. For
example, in step 330, the incoming data stream may be correlated
against a sine wave. For a more detailed description of the
synchronization process 210, section 5.1 Synchronizer is provided
below, which includes subsections 5.1.1 Demodulator, 5.1.1.1
Correlator, 5.1.1.2 Threshold Function, and 5.1.2
Synchronization.
[0048] FIG. 4 illustrates one implementation of the process 220 of
FIG. 2 for constructing a frequency-domain equalization and noise
filter. Constructing the filter typically includes identifying
noise samples, the received preamble, and predefined transmitted
preamble (step 410), computing a noise filter (step 420), a channel
filter (step 430), and ultimately a filter that is applied to the
data frame (step 440). For a more detailed description of the
filter construction process, section 5.2 Filter is provided below
with subsections 5.2.1 Noise Filter, 5.2.1.1 Noise Filter
Computation, 5.2.2 Channel Filter, 5.2.2.1 Raw Channel Filter
Computation, 5.2.2.2 Excision, and 5.2.2.3 Smoothing.
[0049] A channel filter may be computed (step 430) as described
more fully with respect to FIG. 5. Computing a channel filter
typically includes computing the raw coefficients (step 510),
performing excision (step 520) and performing smoothing (step 530).
For a more detailed description of the channel filter construction
process, sections 5.2.2.1 Raw Channel Filter Computation, 5.2.2.2
Excision, and 5.2.2.3 Smoothing are provided below.
[0050] FIG. 6 illustrates a system for digital equalization. The
digital equalization system typically receives an incoming signal
610 (e.g., an analog signal) and applies a receiver/over-sampler
module (e.g., an analog-to-digital converter) 620, a synchronizer
630, a demodulator module 640, a synchronization module 650, and a
filter coefficient module 660.
[0051] 5.1 Synchronizer 630
[0052] The synchronizer 630 generally is capable of detecting the
preamble of the incoming data. The synchronizer 630 typically
includes two components: a demodulation/correlation module (maximum
likelihood at the bit level) and a synchronization module 650
(maximum likelihood for the preamble).
[0053] 5.1.1 Demodulator Module 640
[0054] The demodulator module 640 generally is capable of
converting over-sampled incoming data into a single binary
bitstream of "ones" and "zeroes" (i.e., BitOut). The demodulator
module 640 typically includes a correlator 641 and a threshold
detector 643.
[0055] 5.1.1.1 Correlator 641
[0056] The sampled data stream from the analog-to-digital converter
(ADC) 620 goes into the correlator 641. For each new sample
S.sub.j, N samples are correlated against a stored copy of the
transmitted bit waveform 642 (e.g., in one implementation, a sine
wave is used). Thus, for comparison, the transmitted bit waveform
642 is effectively moved across the input data stream like a
sliding window, for example, effecting the logic of Equations 1 and
2:
BitWeight = i = 0 N - 1 receivedBit [ i ] .times. transmittedBit [
i ] ( Equation 1 ) with transmittedBit [ n ] = { sin ( 2 .pi. n / N
) fortransmitting 1 - sin ( 2 .pi. n / N ) fortransmitting 0 n = 0
N - 1 ( Equation 2 ) ##EQU00001##
[0057] and where N is the over-sampling rate.
[0058] The BitWeight generated by the correlator 641 generally has
a new value for every sample S.sub.j; it is forwarded to a decision
logic (the threshold detector 643) that determines if the
transmitted bit is "0" or "1" based on the correlation of the
sampled region to the pre-stored transmitted bit preamble as
reflected by the BitWeight.
[0059] 5.1.1.2 Threshold Function
[0060] The resultant BitWeight is passed to the threshold detector
643 to be processed. The values are compared to defined max and min
thresholds (both defaulted to "0").
[0061] The threshold detector 643 will convert each BitWeight value
into one logical data bit value. This process is shown in the
following equation:
TABLE-US-00001 If (BitWeight > max_threshold) BitOut = 1; //the
received bit is a "1" If (BitWeight < min_threshold) BitOut = 0;
//the received bit is a "0" Else BitOut = random generated 0 or 1
//no decision can be made (Equation 3)
[0062] 5.1.2 Synchronization Module 650
[0063] The synchronization module 650 detects the beginning and/or
the end of the frame. For each new sample the receiver 651 receives
the BitOut sequence and the comparing device 653 computes the
Hamming distance, d.sub.j, between the received symbol sequence
(received preamble) and the transmitted symbol sequence
(transmitted preamble). The smallest value of d.sub.j for all
samples S.sub.j is called the minimum distance d.sub.minfolding,
which may be used to indicate the frame position (e.g., a maximum
likelihood detection). The position j for which the Hamming
distance is lowest generally is considered the sync position.
[0064] The value of the Hamming distance may be passed to the
folding function module 654 which may convert the Hamming distance
from a [0 . . . 64] range number (e.g., a number corresponding to
the number of bits in the preamble) into "Folded Hamming distance"
which includes a [0 . . . 32] range number according to the
following equation:
TABLE-US-00002 if Hamming distance > 32 then Folded Hamming
distance = 64 - Hamming distance else Folded Hamming distance =
Hamming distance.
[0065] The folding function module 654 enables the detection of
both positive and negative phases of the transmitted signal. For
example, a phase inversion may occur if the transmitter or receiver
is incorrectly plugged into a power outlet. In this instance, the
transmitted bits may be inverted in sign causing a "match"
condition to correspond to maximum Hamming distance values instead
of the minimum Hamming distance values. By folding the Hamming
distance, both positive and negative phases of the transmitted
signal may be detected by examining the minimum Hamming distance
values. The position for which the Folded Hamming distance is
minimal d.sub.minfolding may be considered as the sync
position.
[0066] 5.2 Filter Coefficient Module 660
[0067] The Filter Coefficient Module 660 generates frequency-domain
coefficients that will be used to filter the received data stream
that are useful in recovering the information content of the
transmitted data. In one implementation, for example, the filter
coefficient module 660 also may be implemented using an Adaptive
Filter Coefficient Engine (AFCE).
[0068] In one implementation, the coefficient generation includes
determination of channel, noise filters, and ultimately the complex
multiplication (i.e., frequency-domain operation) of the channel
and noise filters, as described below with respect to Equation
4.
FilterCoeffs(f)=ChannelFilter(f).times.NoiseFilter(f) (Equation
4)
[0069] 5.2.1 Noise Filter 661
[0070] The noise filter generation module 661 may include a filter
capable of minimizing the effect of the noise generated by the
transmission channel. The noise filter generation module 661
generally is based, at least in part, on a sampling of noise from
within the quiet period where no data is transmitted, which
generally precedes the preamble. It also may be based on data
within the preamble identified through synchronization. The sampled
noise and/or preamble generally are combined to construct the noise
filter itself. This approach is valid as long as the frame length
is short enough that the noise could be considered as
quasistationary for the frame duration. For the purposes of this
example implementation, it may be assumed that the noise is
uncorrelated to the data and that the noise is considered
quasistationary for the frame duration.
[0071] 5.2.1.1 Noise Filter Computation
[0072] The noise filter may be computed based on equation 5:
NoiseFilter ( f ) = received preamble ( f ) 2 - noise ( f ) 2
received preamble ( f ) 2 ( Equation 5 ) ##EQU00002##
[0073] where |received preamble(.sub.f)|.sup.2 represents the power
of individual frequency components comprising the discrete spectrum
for the received preamble and where |noise(f)|.sup.2 may be
determined using equation 6:
|noise(f)|.sup.2=min(|noiseRx.sub.--1(f)|.sup.2,|noiseRx.sub.--2(f)|,.su-
p.2 . . . , |noiseRx.sub.--k(f)|.sup.2) (Equation 6)
[0074] where |noiseRx.sub.--1(f)|.sup.2,
|noiseRx.sub.--2(f)|.sup.2, . . . , |noise Rx_k(f)|.sup.2 are
obtained based on noise snapshots from within the IFG, and .sub.k
is the number of noise blocks that are captured. As equation 6 is
based on the minimum of the power of the individual noise frequency
components of the noise streams, it represents a conservative
approach (i.e., versus using an average) to characterizing the
noise for the noise filter computation. If k is too large, the
|noise(.sub.f)|.sup.2 component may tend toward "0" for all
frequencies counteracting the effect of this conservative
characterization of noise. As such, k generally is set to a
relatively small value, e.g., 3.
[0075] 5.2.2 Channel Filter 662
[0076] The channel filter 662 may be a filter capable of
characterizing the channel and compensating for the channel
attenuation and distortion. The channel may be characterized by
comparing the received preamble spectrum against the transmitted
preamble spectrum. Then, the raw channel filter may be computed by
dividing the spectrum of the transmitted preamble bit sequence by
the spectrum of the received preamble bit sequence.
[0077] Once the raw channel filter has been obtained, additional
steps/processing may be performed to eliminate additional
interference (e.g., the narrow band interference) and to increase
the accuracy of the estimate in the presence of noise.
[0078] 5.2.2.1 Raw Channel Filter Computation Module 6621
[0079] One possible equation for computing the raw channel filter
is described as follows. The channel filter 662 may be computed
based on frequency-domain division (i.e. complex number division)
of the prestored/predetermined transmitter preamble by the
received/detected preamble. This is illustrated in the following
equation:
RawChannelFilter ( f ) = prestored / predetermined transmitted
preamble ( f ) received / detected preamble ( f ) ( Equation 7 )
##EQU00003##
[0080] For instance, the raw channel filter may be modified by
post-processing functions identified as "excision" 6622 and
"smoothing" 6623. The excision processing 6622 reduces the gross
anomalies of the spectrum, while the smoothing processing 6623
increases the accuracy of the estimate in the presence of
noise.
[0081] In the implementation described below, excision is performed
before smoothing. However, either could be performed independently,
or the order could be reversed.
[0082] 5.2.2.2 Excision Module 6622
[0083] Excision 6622 may be used to significantly improve the
signal-to-noise ratio by eliminating the narrow-band interference
at the receiver. Several excision algorithms generally are known to
those of ordinary skill, as have been described. See Analysis of
DFT-Based Frequency Excision Algorithms for Direct-Sequence
Spread-Spectrum Communications. IEEE Transactions on
Communications, vol. 46, No. 8, August 1998. Jeffrey A. Young, and
James S. Lehnert.
[0084] When using the excision algorithm, the frequency bin(s) to
be excised is/are generally identified according to their relative
ranking under a predetermined ranking scheme, and the magnitude and
method of excision or notching is determined.
[0085] In one implementation, the frequency bins having a power
value that places them within a selectively high percentage (e.g.,
the top M percent) of the frequency bins are identified for
excision, during which the power values for those frequency bins
are reduced by a selectable amount or ratio (e.g., half). More
specifically, according to one exemplary implementation of the
excision module 6622, power values for the raw channel filter
coefficients 6621 are calculated and sorted, and the top M percent
of the raw channel filter coefficients are selected and divided by
two. An example implementation is illustrated in the following
equation:
TABLE-US-00003 if (RawChannelFilter(f.sub.i)*noiseFilter(f.sub.i))
in top M% then ExcisedChannelFilter(f.sub.i) =
ExcisedChannelFilter(f.sub.i)/2 Else ExcisedChannelFilter(f.sub.i)
= ExcisedChannelFilter(f.sub.i) i = 0...size of the FFT filter -
1
[0086] In this implementation, the excised channel filter spectrum
is subject to a smoothing function following the excision.
[0087] 5.2.2.3 Smoothing Module 6623
[0088] The smoothing function performed by smoothing module 6623
can be summarized by the following logic:
if the power of the |ExcisedChannelFilter(f.sub.i)|.sup.2 component
is greater than the power of the
|ExcisedChannelFilter(f.sub.i+1)|.sup.2 component, then the final
channel filter for bin "i" will be assigned the
ExcisedChannelFilter(f.sub.i+1) bin value.
[0089] An algorithm implementing this logic is as follows:
TABLE-US-00004 if|ExcisedChannelFilter(f.sub.i)|.sup.2
.ltoreq.|ExcisedChannelFilter(f.sub.i+1)|.sup.2
channelFilter(f.sub.i) = ExcisedChannelFilter(f.sub.i) else
channelFilter(f.sub.i) = ExcisedChannelFilter(f.sub.i+1)
[0090] where f.sub.i cover both the negative and the positive
spectrum of the signal.
[0091] In one implementation, following calculation of the noise
filter coefficients and the channel filter coefficients, a
multiplier 663 may be used to generate the ultimate coefficients by
performing complex multiplication of the channel and noise filter
coefficients, as described above with respect to equation 4, such
that a filter may be constructed using the filter computation
module 670.
[0092] 5.2.3 Data Filtering
[0093] FIG. 7 illustrates an overview of the system 700 used to
generate and apply a filter. In one implementation, the
overlap-save method may be used to filter the data. The
overlap-save method performs a linear convolution between a
finite-length sequence (the channel filter coefficients) and an
infinite-length sequence (the data) by appropriately partitioning
the data. This type of data filtering method generally includes an
input module 710, which receives the input data x(n). The input
module 710 may concatenate new input data x(n) with old input data.
The concatenated data may be sent to a first Fast Fourier Transform
(FFT) module 720, where it is used to produce an output X(f).
[0094] Component parts of the input data x(n) (e.g., a preamble (n)
and noise (n)) may be received at a filter coefficient computation
module 730. The filter coefficient computation module 730 also may
receive, as input, a copy of the transmitted preamble 740. The
filter coefficient computation module 730 computes the appropriate
filters and sends its output to a second FFT module 750, which
outputs coeff (f). The output X(f) from the first FFT module 720 is
multiplied with the output coeff (f) from the second FFT module 750
at multiplier module 760.
[0095] The output of multiplier module 760 Y(f) is received at an
Inverse FFT module 770, where an inverse FFT process may be
performed. The result of the inverse FFT process is saved in a
buffer module 780 and the output filtered data y(n) is
produced.
[0096] Additional improvement(s) and a lower Bit Error Rate may be
obtained by pre-filtering the incoming raw data and/or adding
additional hardware that further solves the multipath propagation
problem.
[0097] 6.1 Pre-Filtering
[0098] Pre-filtering may be performed independently and
exclusively, or, in conjunction with of any other filtering
processes, such as the filtering processes described above.
[0099] As described in section 5.2, the filter coefficient
computation may be performed by constructing a channel inverse.
This computation generally requires synchronization on the raw
signal. Indeed, if the signal is jammed or severely corrupted by
noise (e.g., white noise, periodic noise, etc.), the synchronizer
could fail and the frame synchronization may not be detectable.
[0100] In one implementation, the raw data may be pre-filtered to
improve the synchronization capability and thus improve the
filter/equalizer performance (section 5.2). Indeed, since the
filter/equalizer process uses the raw received preamble to compute
the channel inverse, the computed filter coefficients will
generally improve with improvements to estimates of the preamble
position (the sync position).
[0101] More specifically, to pre-filter, the dead time between the
blocks may be used to sample the noise and to construct an adaptive
noise-canceling filter that is helpful in making preamble bits more
identifiable.
[0102] Several noise filters could be used for this purpose. For
instance, in one implementation, a noise filter used for
pre-filtering includes a hybrid version of a "Prediction" filter,
where the desired response for the adaptive filter is the inverse
of the present input signal value.
[0103] FIG. 8 illustrates an example implementation of a
pre-filtering system. The pre-filtering system of FIG. 8 receives
an input signal u(t) that is received at delay module 810, and
includes an adaptive filter module 820 and a summation module
830.
[0104] In this implementation, the following notations are used for
convenience: [0105] s=transmitted signal (uncorrupted by noise),
[0106] n=channel noise (white noise, periodic noise . . . ), [0107]
n=estimate of the channel noise, [0108] u=input applied to the
adaptive filter, [0109] y=output of the adaptive filter, [0110]
d=desired response of the filter, [0111] e=-(y+d)=estimation
error.
[0112] This adaptive filter 820 predicts the inverse of the present
value of the random input signal, even though past values of the
signal supply the input applied to the adaptive filter. The present
value of the signal serves the purpose of the desired response for
the adaptive filter 820.
[0113] When the filter adaptation algorithm (e.g., least mean
square algorithm) is enabled, the adaptation process is designed to
cancel the input signal u(t) by adjusting the filter coefficients
such that y(t)=u(t) u(t)+the filter output="0". Assuming that
during this adaptation process, u(t) was selected such that it is
equal to the noise in the transmission channel (i.e. u(t)=n(t)=the
sampled noise during the dead time between the frame(s)), the
adaptation process will basically construct a noise-canceling
filter that cancels the transmission channel noise such that
y(t)=n(t)-n(t).apprxeq.0.
[0114] Once the adaptation process is complete, the adaptation
algorithm may be disabled and the obtained filter coefficients
locked. Assuming that the noise is quasistationary for the frame
duration (see assumption above), the obtained filter could then be
considered as a noise-canceling filter that is continuously
predicting the inverse of the noise in the transmission channel
y(t)=n(t)-n(t).apprxeq.0. Thus, when data is transmitted
u(t)=s(t)+n(t) the filter will cancel the noise n(t) and let pass
the transmitted data such that
y(t)=s(t)+n(t)-n(t).apprxeq.s(t).
[0115] In another implementation of a pre-filter, for example, if
during the period of the preamble, u(t) is selected such that it is
equal to the transmitted preamble, then the adaptation process may
construct an equalization filter.
[0116] 6.2 Multipath Propagation
[0117] In one implementation, a modem may be designed to transmit
on two paths (e.g., a first may be a Line-Neutral path and a second
may be a Neutral-Ground path). In theory, the transmitted data
(e.g., equalized signals) could be recovered by sampling either
path. However, the two paths may have different characteristics due
to the difference in the path loads (time-varying values),
impedance differences, length differences, and/or noise levels.
This frequency-selective fading of the transmission medium could
cause amplitude and delay distortion which may degrade the system
reliability beyond that expected. It therefore may be difficult to
determine which path is the best one to use to recover the
transmitted data, whether it is the first path, the second path, or
a combination of both paths.
[0118] FIG. 9 illustrates one example implementation of a multipath
propagation system capable of automatically determining which path
or combination of paths is preferred, or optimal. The system may
include structures that automatically probe/sample the paths (e.g.,
Line-Neutral 910 and Neutral-Ground 920), store the data (e.g., by
using one or more buffers 930a, 930b), and synchronize (e.g., using
synchronizers 940a, 940b) on each path, independently. Further,
once a synchronizer 940a, 940b has been detected, the system
automatically adjusts for the delay difference between the paths by
adjusting the read pointer in the buffers 930a, 930b and then
combines the data into a single data stream following gain
adjustment module 950.
[0119] Indeed, assuming that the buffers are large enough, if we
are able to synchronize on both paths 910, 920 (discussed above in
section 5.1), the difference in the sync position typically is
equal to the difference in the path delays. Thus, the solution
presents itself in two parts: first, the synchronizers may be used
to compensate for the path delay differences, and second, the
filter/equalizer discussed above in section 5.2 may be used to
compensate for the distortion. It will be understood that various
modifications may be made without departing from the spirit and
scope of the claims. For example, the order of the operations (path
selection and filtering/equalization) could be changed. Indeed, the
data filtering/equalization part could be done on each path
independently then data could be combined.
[0120] If the attenuation/distortion on either path is too severe
such that no synchronization is possible, a single path solution
may be realized without any path combination. In another
implementation, the addition of "combination thresholds" may refine
this feature even further by enabling the two paths to be combined
if and only if the quality of the received data on the paths is
above a predefined level.
[0121] The described systems, methods, and techniques may be
implemented in digital electronic and/or analog circuitry, computer
hardware, firmware, software, or in combinations of these elements.
Apparatus embodying these techniques may include appropriate input
and output devices, a computer processor, and a computer program
product tangibly embodied in a machine-readable storage device for
execution by a programmable processor. A process embodying these
techniques may be performed by a programmable processor executing a
program of instructions to perform desired functions by operating
on input data and generating appropriate output. The techniques may
be implemented in one or more computer programs that are executable
on a programmable system including at least one programmable
processor coupled to receive data and instructions from, and to
transmit data and instructions to, a data storage system, at least
one input device, and at least one output device. Each computer
program may be implemented in a high-level procedural or
object-oriented programming language, or in assembly or machine
language if desired; and in any case, the language may be a
compiled or interpreted language. Suitable processors include, by
way of example, both general and special purpose microprocessors.
Generally, a processor will receive instructions and data from a
read-only memory and/or a random access memory. Storage devices
suitable for tangibly embodying computer program instructions and
data include all forms of non-volatile memory, including by way of
example semiconductor memory devices, such as Erasable Programmable
Read-Only Memory (EPROM), Electrically Erasable Programmable
Read-Only Memory (EEPROM), and flash memory devices; magnetic disks
such as internal hard disks and removable disks; magneto-optical
disks; and Compact Disc Read-Only Memory (CD-ROM). Any of the
foregoing may be supplemented by, or incorporated in,
specially-designed ASICs (application-specific integrated
circuits).
[0122] Advantageous results still could be achieved if steps of the
disclosed techniques were performed in a different order and/or if
components in the disclosed systems were combined in a different
manner and/or replaced or supplemented by other components. For
example, the transmission medium may include a power line, a
telephone line, a cable line, a digital subscriber line (DSL), an
integrated services digital network (ISDN) line, a radio frequency
(RF) medium, and/or other transmission media. Additionally and/or
alternatively, different modulation schemes are also available for
application to the digital equalization process. For example, a
combination of modulation schemes may be applied to different parts
of the signal (e.g., one modulation scheme may be applied to the
preamble and a different modulation scheme may be applied to the
remainder of the signal). In another implementation, for example,
in a multipath propagation implementation, different modulation
schemes and/or combinations of different modulation schemes may be
applied to each signal on the different communication paths.
Accordingly, other implementations are within the scope of the
following claims.
* * * * *