Note: Descriptions are shown in the official language in which they were submitted.
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JOINT SPACE-TIME OPTIMUM FILTERS (JSTOF) FOR INTERFERENCE
CANCELLATION
Field of the Invention
The present invention relates to wireless communications systems, such as
cellular
communications systems, and, more particularly, to filtering received wireless
signals to
reduce unwanted interference.
Background of the Invention
Interference canceling matched filters (ICMF) and joint demodulation (JDM) has
been investigated to meet requirements for a Downlink Advanced Receiver
Performance
(DARP) that is standardized by the third generation mobile communications
system and
the Third Generation Partnership Project (3GPP). Some of these proposals are
set forth in
the following articles and documents.
1. Liang et al., A Two-Stage Hybrid Approach for CCI/ISI Reduction with
Space-Time Processing, IEEE Communication Letter Vol. 1, No. 6, Nov. 1997.
2. Pipon et al., Multichannel Receives Performance Comparison In the
Presence of ISI and CCI, 1997 13th Intl. Conf. on Digital Signal Processing,
July
1997.
3. Spagnolini, Adaptive Rank-One Receiver for GSM/DCS Systems, IEEE
Trans. on Vehicular Technology, Vol. 51, No.5, Sept. 2002.
4. Feasibility Study on Single Antenna Interference Cancellation (SAIC) for
GSM Networks, 3GPP TR 45.903 Version 6Ø1, Release 6, European
Telecommunications Standards Institute, 2004.
5. Radio Transmission and Reception (Release 6), 3GPP TS 45.005 Version
6.8.0; European Telecommunications Standards Institute, 2005.
6. Stoica et al., Maximum Likelihood Parameter and Rank Estimation in
Reduced-Rank Multivariate Linear Regressions, IEEE Trans. On Signal
Processing, Vol. 44, No.12, Dec. 1996.
7. Kristensson et al., Blind Subspace Identification of a BPSK
Communication Channel, Proc. 30th Asilomar Conf. On Signals, Systems and
Computers, 1996.
8. Golub et al., Matrix Computations, 3'd Edition, 1996.
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9. Trefethen et al., Numerical Linear Algebra, 1997.
10. Press et al., Numerical Recipes in C, 2d Edition, 1992.
Current Global System for Mobile communications (GSM) cellular systems have
to address the co-channel interference (CCI) on the mobile station (MS) side,
as well as
address the DARP requirements. Some single channel structures and pre-filters
have been
used to aid in canceling the interference and provide some channel impulse
response (CIR)
estimation. Moreover, some systems have used maximization of the signal-to-
interference
to design jointly a single channel space-time filter and the CIR estimation
for a single
channel. Other systems have used a constrained minimization of the mean-square
error to
design a single channel space filter. Other systems have used a single channel
space filter
that is designed by a rank-one approximation of the ML channel estimation. The
target
applications for these systems have been a base station where a physical
antenna array
including a plurality of antennas is available.
Brief Description of the Drawiniis
Various objects, features and advantages will become apparent from the
following
detailed description when considered in light of the accompanying drawings, in
which:
FIG. 1 is a block diagram of a Joint Space-Time Optimum Filter based Downlink
Advanced Receiver Performance (DARP) capable receiver in accordance with an
embodiment of the invention;
FIG. 2 is a more detailed block diagram of the Joint Space-Time Optimum Filter
and Multi-Channel Matched Filters shown in FIG. 1;
FIG. 2A is a block diagram of a method in accordance with the present
invention;
FIG. 3 is a graph showing the Joint Space-Time Optimum Filter based DARP
capable receiver performance for various DARP test cases;
FIG. 4 is a graph showing the Joint Space-Time Optimum Filter receiver
performance in accordance with the present invention with additive white
Gaussian noise
(AWGN), compared with and without an auto-switching strategy;
FIG. 5 is a graph showing the Joint Space-Time Optimum Filter receiver
performance in accordance with the present invention with DTS-5, compared with
and
without auto-switching;
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FIG. 6 is a graph comparing the performance of single with multiple Viterbi
equalizers in accordance with the present invention, using 8-bit SD limiter in
the
simulation;
FIG. 7 is a graph showing the performance of Joint Space-Time Optimum Filter
Receiver and a modified test case in accordance with the present invention;
FIG. 8 is a schematic block diagram of an exemplary model wireless
communication device that can be used in accordance with one embodiment of the
present
invention; and
FIG. 9 is a table comparing the three approaches for performing Cholesky, QR,
and SVD computations in accordance with the present invention.
Detailed Description of the Preferred Embodiments
Several non-limiting embodiments will now be described more fully hereinafter
with reference to the accompanying drawings, in which preferred embodiments
are shown.
These embodiments may, however, be embodied in many different forms and should
not
be construed as limited to the embodiments set forth herein. Rather, these
embodiments
are provided so that this disclosure will be thorough and complete, and will
fully convey
the scope to those skilled in the art. Like numbers refer to like elements
throughout, and
prime notation is used to indicate similar elements in alternative
embodiments.
In accordance with one embodiment, Co-Channel Interference (CCI) on a mobile
station (MS) side in a current Global System for Mobile (GSM) communications
system is
addressed, as well as the compliant requirement of a Downlink Advanced
Receiver
Performance (DARP) standard by the Third Generation Partnership Project
(3GPP).
The invention may generally be summarized as follows. A filter reduces co-
channel interference within a conununications receiver and may include a multi-
channel,
space-time filter circuit that filters signal parts that have been split from
a communications
signal by jointly estimating space-time filter weights and multi-channel
impulse responses
(CIRs). A multi-channel matched filter circuit may receive multi-channel
signals from the
multi-channel, space-time filter circuit and have a filter response that is
provided by a
channel impulse response estimation from the space-time filter circuit. An
alternative filter
may be operative when an interference level is below a pre-detennined
threshold and may
be formed as a matched filter and cross-correlation circuit and switch
mechanism for
switching the signal parts into the matched filter and cross-correlation
circuit.
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In one aspect, the multi-channel, space-time filter circuit may include a
plurality of
multiplier and delay circuits that each receive n signal parts. The multiplier
and delay
circuits rnay be operative based on space-time filter weights. More
particularly, two
multiplier circuits connected in parallel and a delay circuit connected to the
input of one of
the multiplier circuits may be used for each signal part. Each multiplier and
delay circuit
may have about a one symbol delay. A joint optimal filter weights and channel
estimator
may be operatively connected to the multi-channel, space-time filter circuit
and receive
training sequence (TS) symbols and timing uncertainty data and generate space-
time filter
weights for the multi-channel, space-time filter circuit. A summer circuit may
sum data
from the multiplier and delay circuits for each channel. An equalizer circuit
may be
operative with the multi-channel, matched filter circuit.
The illustrated embodiment in FIG. 1 provides a multi-channel pre-filter that
is
operable for canceling interference and providing channel impulse response
(CIR)
estimation adaptively and optimally. The pre-filter can use two major
components in one
non-limiting example: (1) a multiple-input-multiple-output (MIMO) based Joint
Space-
Time Optimum Filter (JSTOF); and (2) a multiple-input-single-output (MISO)
based
multi-channel matched filter. In a typical mobile station using a single
available antenna, a
virtual antenna array can be configured internally by the combination of over
sampling
and the separation of real and imaginary parts that receive samples, as will
be appreciated
by those skilled in the art.
In one non-limiting embodiment, a signal from the virtual antenna array is fed
to
the JSTOF, where the optimum weights for the MIMO-based interference canceling
filter
are estimated. At the same time, the multi-channel CIRs for the desired signal
are jointly
estimated. The output of the JSTOF allows the interference to be filtered and
fed to a
MISO-based multi-channel matched filter. The filter response of the matched
filter is
provided by the CIR estimation from the JSTOF.
The output of the multi-channel matched filter passes to a Viterbi equalizer
which
removes the inter-symbol interference (ISI) and provides soft decisions for
further
processing. A single channel response required by the equalizer can be formed
by a
combination of the convolved CIRs from the JSTOF. This pre-filter can also
automatically
switch to the conventional or standard filter in the conventional receiver in
any AWGN
dominant cases and switch back to the JSTOF-based receiver in any interference
dominant
cases. This auto-switching capability reduces the loss in AWGN dominant cases.
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An example of the pre-filter or interference canceling filter for the JSTOF-
based
and DARP-capable receiver is shown at 10 in FIG. 1, in which the oversampling
ratio is 2
and the number of the virtual antennas is 4 (M = 4), as also indicated by
Xl(k) through
X4(k). Throughout this description the pre-filter 10 can be referred to as the
interference
canceling filter or JSTOF filter, and acts as a pre-filter in a DARP compliant
receiver. A
receiver incorporating this filter 10 could be described as a JSTOF receiver
as is shown by
the dashed line at 11 in FIG. 1.
FIG. 1 shows examples of the various circuit blocks used for the filter 10. An
input
signal is received into a derotation circuit 12. The derotated output signal
is split, with a
portion passing into a filter 14 of a conventional receiver that includes a
2:1 switch 16, and
an output into a matched filter 18 and a cross-correlation circuit 20 that
receives shortened
training sequence (TS) symbols. The 2:1 switch 16 is operable to allow
switching between
the filter 14 and the JSTOF-based and DARP-capable pre-filter 10.
The other portion of the output signal from the derotation circuit 12 is split
into
even samples and odd samples as part of the virtual antenna 24 and split again
into real
and imaginary signals to form the respective Xl(k) through X4(k) input signals
into a
JSTOF circuit 30, also referred to as a multi-channel, space-time filter
circuit. It should be
noted that a virtual antenna arrangement need not be used in all embodiments.
That is, the
filter 18 may be used for filtering signals directly from one or more physical
(i.e., real)
antennas, such as in a phase shift keying (PSK) application (e.g., 8PSK), for
example. The
output signals from the JSTOF circuit are passed into a multi-channel matched
filter
circuit 32, and its output signal is passed into a rescaling circuit 34 and
then into a
multiplexer circuit 36 as data (di). The multiplexer circuit 36 also receives
a channel (cl)
response. When the conventional filter 14 is connected, the multiplexer 36
receives the
data (d2) and channel (c2) response from the matched filter circuit 18 and
cross-correlation
circuit 20. Signals are passed into a Viterbi equalizer 38 as a soft decision
output.
Further details of the JSTOF and the multi-channel matched filters are shown
in
FIG. 2, where the number of time-delayed samples used in the JSTOF circuit is
2 (N = 2).
The various inputs XI(k) through X4(k) are received into the JSTOF, which is
shown in
greater detail. The JSTOF circuit 30 includes channelized multipliers, also
termed mixers
40, 42, delay units 44 and summers 46, which input into multi-channel matched
filters 48
for each of the four illustrated channels, and signals from the matched
filters are passed
into a summer 50. A joint optimal filter weights and channel estimator circuit
52 receives
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TS symbols and timing uncertainty signals to produce the weights (WopT) used
for the
mixers 40, 42.
It thus is possible as described to integrate a pre-filter function into a
conventional
GSM receiver by adding a pre-filter branch parallel to a conventional matched
filter as
shown in FIG. 1. The conventional software/hardware Viterbi equalizer 38 can
be used
without change. In one non-limiting example, an integrated DARP-capable
receiver has
been tested against DARP test cases by simulations, which indicates that the
receiver
provides 1.6 dB to 6.9 dB margin over a specified performance in terms of the
frame error
rate (FER) for one of the AMR speech channels.
FIG. 2a is a flow chart illustrating a high-level method associated with the
described system in which the various steps are shown as non-limiting
examples. The
various steps begin with the 100 series reference numerals. The incoming
communications
signal is derotated (Block 100) and passed into the virtual antenna. The
communications
signal is split into even and odd samples (Block 102), and each even and odd
sample is
then split into real and imaginary signal parts (Block 104). The
communications signals
from the virtual antenna are passed into the JSTOF circuit, where the
communications
signals are multiplied and delayed (Block 106) and then summed (Block 108),
all as part
of a first multiple-in, multiple-out (MIMO) Joint Space Time Optimum Filter
(JSTOF).
After summing, summed signals are passed into the multi-channel, multiple-
input single-
output (MISO) matched filter circuit (Block 110) and then summed (Block 112)
and
passed as a single out signal into the Virterbi equalizer (Block 114) in which
a soft
decision is made (Block 116).
In operation, the derotation circuit 12 is operable with GMSK modulated
signals
and the frequency offset that is part of that signaling protocol. Before any
derotation, the
signal constellation is dynamic and after derotation the signal constellation
becomes static,
i.e., any symbols are typically concentrated on 0 and 180 , with symbols
rotated to those
two points. Thus, the GMSK signal can be treated as a typical binary phase
shift keying
(BPSK) signal. The derotation at the front end is used for even and odd
samplings, which
is useful because of the over-sampling rate. For example, in a conventional
receiver, this is
typically at the rate of 1, i.e., one sample per symbol.
The virtual antenna 24 can increase the sampling rate to two samples per
symbol in
a serial manner coming from the baseband filter to form two separate channels
of even and
odd. Before this process, the odd/even samples were serially interleaved.
These signals are
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then further split into the real and imaginary signal parts to form the four
independent
channels of the virtual antenna. It should be noted that in some embodiments
other
numbers of virtual antennas/channels may be used (e.g., one or more), as will
be
appreciated by those skilled in the art.
As best shown in FIG. 2, these signals are then passed into the multiplier
40,42 and
unit delay circuits 44, e.g., a one symbol delay, and thus the signal is
processed with a
multiply and delay, followed by a multiply operation as evident by the two
multipliers 40,
42 and one delay circuit 44. This operation is followed by a summation in
summer 46 as
illustrated. This portion of the system is operable as a multi-channel, two-
dimensional
filter. One dimension occurs because of the delay in time and another
dimension is
introduced from the virtual antenna, i.e., a spatial dimension as described
and thus the two
dimensions form a space-time filter.
It is evident each incoming signal is used in conjunction with other channels,
and
multipliers receive weights from the Joint Optimal Filter Weights and Channel
Estimator
52. The weights coming from the Joint Optimal Filter Weight and Channel
Estimator 52
are passed into the multipliers.
The weights are also an 8 x 4 dimensional matrix in one non-limiting example,
i.e.,
32 weights. As to the training sequence symbols input into the Joint Optimal
Filter
Weights and Channel Estimator 52, there are typically in some non-limiting
examples
about 26 known symbols and it is known which training sequence a packet
contains. A+/-
3 or seven positions search in a non-limiting example can be used to find the
timing. The
impulse response of the multi-channel matched filter (hopt) can be used such
that the
system matches the channel response and makes the signal stronger after the
matched
filter.
As shown in FIG. 1, rescaling can occur as a hardware or software convenience,
although it is not required. This rescaling circuit 34 allows greater
operation for a 4-bit or
5-bit input as a non-limiting example to the Viterbi equalizer 38. The dynamic
range of the
signal can be readjusted such that the signal can be sent into a 4-bit or 5-
bit circuit.
As noted before, the multiplexer 36 can take the signals d2 and c2 for the
data and
channel response from the conventional filter receiver 14 or the signals dl
and cl for the
data and channel response from the JSTOF receiver 10 to allow a switch-over
between the
two. The JSTOF receiver will introduce some loss if there is no interference,
i.e., just pure
white noise. In this case the conventional receiver 14 can be used and will
work
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adequately. So, the circuits can switch back to the conventional filter
without loss
introduced by the JSTOF receiver and its circuits. The switching is based on
the estimation
of the SINRouT minus SINR1NP. If the quantity is below a threshold, the system
determines
there is little interference and the interference canceling of the JSTOF
receiver is not
required. Thus, the filter of the conventional receiver 14 is used by
switching the 2:1
switch 16.
The circuit is operable in beam forming systems and other systems. This type
of
system also allows the signal-to-noise ratio to be improved and the bit error
rate (BER) to
be improved. This could impact top level protocols and phone calls and other
communications matters for use with these circuits.
The multi-channel structure of the JSTOF-based filter 10 is used in one
embodiment, and the MIMO-based JSTOF circuit 30 provides a space-time filter
weight
and channel estimations that are different from prior art solutions. This
circuit provides the
ability to combat the interference efficiently for both synchronous and
asynchronous
interferences and yield high performance. Some simulations have shown that
none of the
solutions in some prior art techniques provide the required performance
against the DARP
test cases.
This MISO-based multi-channel matched filter circuit 32 is a feature that
improves
the overall error rate performance and reduces the complexity of the equalizer
by avoiding
multi-channel Viterbi equalizers. The built-in automatic switching between
JSTOF-based
and conventional receivers reduce the loss in AWGN cases.
Suitable receiver structures can be used in order to meet the DARP
requirements.
An Interference Canceling Matched Filter (ICMF) can use an example of the
virtual
antenna as described and beamforming to combat the interference. The circuit
is sensitive
to the estimation errors of the Channel Impulse Response (CIR) of the desired
signal. A
Joint Demodulation (JD) showed good performance for the various test cases. In
addition
to the difficulty in combating the asynchronous interferers, there may be
heavy
computational complexity involved in finding the CIR of an interferer.
In one embodiment, the virtual antenna 24 is operable with adaptive space-time
filtering, allowing the Joint Spatial-Temporal Optimum Filter (JSTOF) circuit
30 to be
used. One difference from the ICMF is that the spatial-temporal filter weights
used to
suppress the interference and the CIR estimation of the desired signal are
jointly estimated
and optimized in the JSTOF while the two are separately estimated in an ICMF.
The
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JSTOF circuit 30 can be a Multiple-Input-Multiple-Output (MIMO) circuit that
takes
advantage of the rank deficiency nature of the desired CIR matrix in the space-
time setup.
Simulations have shown a satisfactory performance for the various DARP test
cases.
Computational load is deemed acceptable given that fixed-point Cholesky
factorization
and EVD/SVD are feasible.
This method has some simplicity and low computational complexity. It is also
robust because the system makes few assumptions about the source of the
interference. In
addition, the system can continue to use the existing equalizer structure, as
the solution is
integrated as a pre-processing step on the input data. This would allow the
system to use
the HW equalizer accelerators if available.
In order to support the evaluation of this technique, the system level Block
Error
Rate (BLER) simulator was extended to support all of the interferer
models/scenarios
being used by the 3GPP DARP Specification.
There now follows a description of the simulation performance for DARP test
cases using the JSTOF circuit. It should be understood that space-time
processing for joint
interference reduction and channel estimation has been used in a base station,
where an
array of M antennas is available. Assuming that the equivalent channel
response for the
single desired user can be modeled as an L-tap Finite Impulse Response (FIR)
filter, a
snapshot sample of the received baseband signal can be expressed as
L-1
x(k) _ E c(1)sk-, + v(k) = Hs(k) + v(k), (1)
1=0
where x(k) is an Mx 1 vector representing the output from the antennas, H is
an MxL
matrix containing the channel response for the antenna array, s(k) is an Lx 1
vector for the
corresponding symbols transmitted, and v(k) is an Mx 1 vector including the
AWGN and
the interference. The space-time extension for formula (1) can be obtained by
stacking N
time-delayed versions of x(k) into a taller MNx 1 vector z(k) as follows:
x(k) = [xT (k), xT (k -1), = = =, xT (k - N + 1)]T = Hs(k) + V(k), (2)
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where H an MNx(L+N-1) matrix is the block Toeplitz version of H and s(k)=[sk,
sk.i, ...,
sk.L_N+2]T. The samples that correspond to the training sequence can be
collected,
[z(k),z(k+l),===,z(k+p-1)]=HS+V, (3)
where p= P - L - N + 2, P is the number of symbols of the training sequence, X
is an
MNxp matrix, and s=[s (k), s(k+1), ..., i(k+p-1)] is an (L+N-1)xp convolution
matrix
of the training symbols. The joint optimization is to find a non-trivial MNx 1
weight vector
w for a space-time filter and a non-trivial (L+N-1)xlchannel estimation vector
h after the
filter such that the output interference residual of the filter is minimized,
i.e., to solve the
following optimization problem:
minilwTX-hTSII2. (4)
W'h
It can be found that the optimal weight is:
wop, = Rx'RxshoPt , (5)
and the optimal channel estimation hop, is the eigenvector corresponding to
the minimum
eigenvalue of the matrix Rs - R~Rx'R~ , where
Rx = X'XT (MNxMN) (6)
Rs = S'ST, ((L+N-1)x (L+N-1)) and (7)
RXS = X*ST, ((MN)x (L+N-1)). (8)
Given that the noise plus interference component V in the space-time model of
equation
(3) is no longer white but approximately Gaussian distributed with unknown
covariance
matrix R, the optimal estimation for the channel H is the maximum-likelihood
(ML)
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estimation, which is a minimization of the following quantity:
i(H,R,) = loglR, I+IIX - HSIIRY, . (9)
In this non-limiting space-time model, the number of the independent channels
is always
less than or equal to M and H is usually rank deficient, i.e., rank( H)= r <
min(MN, L+N-
1). The rank deficient ML problem can be used for the rank-1 approximation of
the space-
time filter.
The JSTOF circuit in one embodiment can use a different approach to find the
joint
optimum solutions for the filter weight and the channel estimation. It is
possible to find the
ML estimation of H. The estimation can be decomposed as
H' =HH', (10)
where HS (MNxM) is the estimation of the space matrix of H and H, ((L+N-1)xM)
is the
estimation of the time matrix of H. They can be obtained by:
H, = RS'i2Vo,N , and (11)
HS = RxsH,, (12)
where RS = Rs1ZRHI2 is the Cholesky factorization and VDM consists of the M
eigenvectors corresponding to the top M eigenvalues of the matrix D,
D = RsH~zRHRx'RxsRsv2 (13)
In a next step, the optimal weight for the space-time filter can be obtained
by
woPt = Rx'RXSHI , (MNxM) (14)
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and the optimal channel estimation is
ha~, = w ~t H . (Mx(L+N-1)) (15)
It is then possible to apply the optimal space-time filter in equation (14) to
the
samples from the antenna array 24. Clearly the outputs of the filter 30 still
have M
channels, and it is a MIMO system. The optimal channel estimation in equation
(15) can
be used for the multi-channel matched filters 32. The outputs of the matched
filter are then
combined (summed up) and rescaled in the rescaling circuit 34 to the modified
desired
level. The final output is a single-channel sample stream and can be fed into
the Viterbi
equalizer 38. Note also that the number of channel taps after the JSTOF has
been changed
to L+N-1 comparing to L of the modeled channel taps before the JSTOF.
It was observed by simulations that the JSTOF receiver incurred more that 1 dB
loss in the pure AWGN cases compared to the conventional receiver using the
conventional filter. To reduce the loss, a strategy of automatic switching
between the
JSTOF and conventional receivers was developed. The switching is based on the
measurement of the difference of the input and output SINR's of the JSTOF.
When the
difference is below a predefined threshold the JSTOF receiver is turned off
and the
conventional receiver is turned on. The input SINR can be easily computed once
the
estimation of H is done in equation (10):
SINR;,, - Fl2 - +tr(H'RSfIT) ~ (16)
p IIX IISIIZ n(RX + H RSHT - 2Re{RxSHr })
and the output SINR can be computed from equations (14) and (15):
IIZ * r
SINR tr(hop;Rshpt) (17)
ou~ Ilw ptX -hoP191I2 tr(w p,Rxw+hop;Rsh pt -2Re{w prRXSh pt})
On the mobile side, a virtual antenna array can be set up by the combination
of
oversampling and the separation of the real and imaginary parts as shown in
FIG. 1.
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In accordance with various embodiments, the joint optimum MIMO space-time
filter and channel estimation set forth in equations (14) and (15) enhances
interference
suppression performance. The MISO multi-channel matched filters 32, which are
based on
the channel estimation in equation (15), improve the error rate performance
while reducing
the complexity of the Viterbi equalizer 38. A strategy of automatic switching
between
JSTOF and conventional receivers reduces the loss in pure AWGN cases.
The JSTOF defined by equations (6)-(17) can be implemented in different ways
in
terms of numerical stability and computational complexity. The major
differences are the
way in which the inverse of the autocorrelation matrix Rx is calculated and
the way in
which the channel H is estimated with reduced rank.
One such implementation is a Cholesky decomposition-based matrix inversion of
Rx and the eigenvalue decomposition of matrix D in equation (13).
Specifically, since
RX is symmetric positive definite, the Cholesky decomposition exists:
Rx = LXLx . (18)
D can be rewritten as
D = D,D; , (19)
where
D, = LSTR sLxT . (20)
It should be noted that the inverse is actually performed with the square-root
of
RX, and the explicit computation of the inverse may be avoided by the back-
substitution.
Also, D is numerically stable because of its structure of mutual
cancellations. This was
verified by simulations that showed the condition number of D is seldom
greater than
300. This implies the eigenvalue decomposition on D would not require unduly
sophisticated algorithms for typical applications, as will be appreciated by
those skilled in
the art. In fact, this approach may potentially have the least computational
complexity of
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the approaches outlined herein.
One potential numerical concern is the Cholesky decomposition on R, as its
condition number may potentially be relatively high, and its positive definite
property may
be offset to some degree by round-off errors. Simulations showed, however,
that the
condition number of Rx is less than 107 even in some extreme scenarios such as
very high
and very low carrier-to-interference (C/I) ratios.
In accordance with an alternate embodiment, the QR decomposition in the sample
domain may be used to avoid the direct calculation of the inverse of R. Since
the XT in
equation (3) has full column rank, it has the unique QR decomposition
XT =QR, (21)
where Q is a pxMN matrix with orthogonal columns and R is a full rank MNxMN
upper
triangular matrix. It can be shown that
RXl _ R-1R-r (22)
and the D in equation (13) can be written in the form of equation (19) with
the D, re-
defined by
D, = LsTSQ . (23)
The reduced rank channel estimation may be performed with the eigenvalue
decomposition on D as in the previous approach, and the optimum filter weight
matrix of
(14) can be reduced as
~ r (24)
wopt =R DIVDM. This approach is basically an equivalent version of Cholesky
decomposition in the
sample domain since one can show that R = Lx L. It has improved numerical
stability at the
expense of the QR decomposition's greater complexity (requiring approximately
twice as
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many operations for a matrix of given size) and larger sample matrix (having
approximately 3 times as many rows in an example case where M=4, N=2 and L=5).
The two approaches described above still require the computation of the
triangular
matrix inverse, although this may be done by back-substitutions. Turning now
to
yet another alternate approach, i.e., the singular value decomposition (SVD)
approach,
the matrix inversion may be avoided and the numerical stability may be further
improved
in some applications. This approach starts with the SVD on the sample matrix
in equation
(3):
XT = UxEzVz , (25)
where Ux is a pxMN matrix with orthogonal columns, VX is an MNxMN orthogonal
matrix and Es is an MNxMN diagonal matrix, EX = diag(6, ,===, 6i1,,,), with
the singular
values on its diagonal. It can be shown that
Rx' = VxEsZVz . (26)
The P in equation (13) still has the form of equation (19) with D, defined by:
D, = LsTSUx . (27)
The channel estimation may be obtained by the SVD on D, and the filter weight
matrix
may be written as
p)
Wopt =VxFx 1D T~V (28)
where VDM contains the top M right singular vectors of D,. The SVD in this
approach
may require more computations than the Cholesky and QR decompositions used in
the
previous two approaches.
As a comparison of the three approaches outlined above (i.e., Cholesky, QR,
and
SVD), the table in FIG. 9 lists the computa.tions step by step for an example
where M=4,
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N=2 and L=5. To find the best timing of the burst, the JSTOF searches a number
of timing
hypotheses and the one corresponding to the minimum output residual is chosen
as the
best timing. The output residual is defined by:
e= IIw opt X- h p, s z
I) ' (29)
The search process basically repeats the operations listed in the table for
each hypothesis,
but the input sample matrices from the consecutive timing hypotheses change
slightly by
appending and deleting a column. The updating and the downdating algorithms
are
potentially applicable to some of the operations, and the overall computation
load may
potentially be reduced.
Let X(k) represent the sample matrix at time instant k. It may be partitioned
from
equation (3) to
X(k) = [z(k), X(k + 1)], (30)
where
X(k+1)=['x(k+1),==.,x(k+p-1)]. (31)
The sample matrix at time k+l may be expressed as
X(k + 1) = [X(k + 1), x(k + p)]. (32)
The autocorrelation matrix at time k+l has the form
Rx (k + 1) = Rx (k) - x(k)xT (k) + x(k + p)xT (k + p). (33)
This is a combination of a rank-1 downdate and a rank-1 update. One hyperbolic
rotation-
based algorithm for updating/downdating the Cholesky factorization is set
forth in Matrix
Computations by Golub et al., 3'd edition, 1996.
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Another applicable update/downdate algorithm disclosed in Golub et al. text is
for
QR decomposition, which is based on the Givens rotation. Of course, the given
approach
that should be used in a particular application will depend on factors such as
available
processing resources, computational complexity, etc., as will be appreciated
by those
skilled in the art. Other approaches may also be used, as will also be
appreciated by those
skilled in the art.
The performance of the JSTOF based receiver has been evaluated by Matlab
simulations using an extended BLER simulation engine. The parameters for the
JSTOF
based receiver can be set with different aspects. Examples of values follow:
1) The oversampling ratio (OSR) of 2 can be selected, which maps to the
number of virtual antennas (M) of 4 in this non-limiting example, and
simulation
shows that reducing the OSR to I causes significant performance degradations;
2) A number of temporal delayed samples (N) can be selected as 2. Increasing
the number, however, does not always improve the performance;
3) A reduced rank for the channel response matrix can be selected as M.
Increasing or decreasing the rank does not necessarily improve the
performance.
4) An auto-switch threshold can be 4.75 dB.
5) A soft decision output can be quantized in 5 bits width. Increasing the
width to 8 bits can improve the performance marginally for DTS-5. Soft
decision
correction can be enabled.
The AMR speech channel, TCH-AFS 12.2 can be used to evaluate the performance
of the JSTOF in terms of FER. The propagation condition TU50km/h-1950MHz can
be
assumed throughout the simulations. A simulation ran 1000 trials (blocks) for
each case.
The FER's of the receiver, against the carrier-to-interference (C/I) ratio,
are shown
in the graph of FIG. 3. The margins against the reference performance
specified are listed
in the table below.
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Test case JSTOF Spec. performance: Margin of JSTOF
performance: C/I C!I at FER = 1%, dB against Spec., dB
atFER=1%,dB
DTS-1 -2.6 4 6.6
DTS-2 7.3 9 1.7
DTS-3 7.6 10 2.4
DTS-4 -0.9 6 6.9
DTS-5 7.4 9 1.6
The performance of the receiver under pure AWGN and DTS-5 cases with and
without the auto-switching strategy is shown in the graphs of FIG. 4 and FIG.
5,
respectively. The strategy reduced the loss in AWGN by about 1 dB (at FER =
10%) and
incurred little loss for DTS-5.
The JSTOF receiver can include multiple Viterbi equalizers, followed by a
multi-
channel match filter, which combines the soft decisions after the equalizers.
A result is
shown and compared with the original in the graph of FIG. 6.
Performance can be evaluated with a modified test case DTS-5R, where the delay
of the asynchronous interferer can be. configured. The performance at 0, '/4,
I/~ and 3/4 of the
burst length is shown in the graph of FIG. 7. The results indicate that the
performance of
JSTOF receiver degrades "slowly" with severe delay of the interferer.
The above-described receiver may advantageously be used in mobile wireless
devices (e.g., cellular devices) as well as cellular base stations, for
example. An example
of a mobile wireless communications device 1000 that may be used is further
described in
the example below with reference to FIG. 8. The device 1000 illustratively
includes a
housing 1200, a keypad 1400 and an output device 1600. The output device shown
is a
display 1600, which is preferably a full graphic LCD. Other types of output
devices may
alternatively be utilized. A processing device 1800 is contained within the
housing 1200
and is coupled between the keypad 1400 and the display 1600. The processing
device
1800 controls the operation of the display 1600, as well as the overall
operation of the
mobile device 1000, in response to actuation of keys on the keypad 1400 by the
user.
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The housing 1200 may be elongated vertically, or may take on other sizes and
shapes (including clamshell housing structures). The keypad may include a mode
selection
key, or other hardware or software for switching between text entry and
telephony entry.
In addition to the processing device 1800, other parts of the mobile device
1000 are
shown schematically in FIG. 8. These include a communications subsystem 1001;
a short-
range communications subsystem 1020; the keypad 1400 and the display 1600,
along with
other input/output devices 1060, 1080, 1100 and 1120; as well as memory
devices 1160,
1180 and various other device subsystems 1201. The mobile device 1000 is
preferably a
two-way RF communications device having voice and data communications
capabilities.
In addition, the mobile device 1000 preferably has the capability to
communicate with
other computer systems via the Internet.
Operating system software executed by the processing device 1800 is preferably
stored in a persistent store, such as the flash memory 1160, but may be stored
in other
types of memory devices, such as a read only memory (ROM) or similar storage
element.
In addition, system software, specific device applications, or parts thereof,
may be
temporarily loaded into a volatile store, such as the random access memory
(RAM) 1180.
Communications signals received by the mobile device may also be stored in the
RAM
1180.
The processing device 1800, in addition to its operating system functions,
enables
execution of software applications 1300A-1300N on the device 1000. A
predetermined set
of applications that control basic device operations, such as data and voice
communications 1300A and 1300B, may be installed on the device 1000 during
manufacture. In addition, a personal information manager (PIM) application may
be
installed during manufacture. The PIM is preferably capable of organizing and
managing
data items, such as e-mail, calendar events, voice mails, appointments, and
task items. The
PIM application is also preferably capable of sending and receiving data items
via a
wireless network 1401. Preferably, the PIM data items are seamlessly
integrated,
synchronized and updated via the wireless network 1401 with the device user's
corresponding data items stored or associated with a host computer system.
Communication functions, including data and voice communications, are
performed through the communications subsystem 1001, and possibly through the
short-
range communications subsystem. The communications subsystem 1001 includes a
receiver 1500, a transmitter 1520, and one or more antennas 1540 and 1560. In
addition,
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the communications subsystem 1001 also includes a processing module, such as a
digital
signal processor (DSP) 1580, and local oscillators (LOs) 1601. The specific
design and
implementation of the communications subsystem 1001 is dependent upon the
communications network in which the mobile device 1000 is intended to operate.
For
example, a mobile device 1000 may include a communications subsystem 1001
designed
to operate with the MobitexTM, Data TACTM or General Packet Radio Service
(GPRS)
mobile data communications networks, and also designed to operate with any of
a variety
of voice communications networks, such as AMPS, TDMA, CDMA, WCDMA, PCS,
GSM, EDGE, etc. Other types of data and voice networks, both separate and
integrated,
may also be utilized with the mobile device 1000. The mobile device 1000 may
also be
compliant with other communications standards such as 3GSM, 3GPP, UMTS, etc.
Network access requirements vary depending upon the type of communication
system. For example, in the Mobitex and DataTAC networks, mobile devices are
registered on the network using a unique personal identification number or PIN
associated
with each device. In GPRS networks, however, network access is associated with
a
subscriber or user of a device. A GPRS device therefore requires a subscriber
identity
module, commonly referred to as a SIM card, in order to operate on a GPRS
network.
When required network registration or activation procedures have been
completed,
the mobile device 1000 may send and receive communications signals over the
communication network 1401. Signals received from the communications network
1401
by the antenna 1540 are routed to the receiver 1500, which provides for signal
amplification, frequency down conversion, filtering, channel selection, etc.,
and may also
provide analog to digital conversion. Analog-to-digital conversion of the
received signal
allows the DSP 1580 to perform more complex communications functions, such as
demodulation and decoding. In a similar manner, signals to be transmitted to
the network
1401 are processed (e.g. modulated and encoded) by the DSP 1580 and are then
provided
to the transmitter 1520 for digital to analog conversion, frequency up
conversion, filtering,
amplification and transmission to the communication network 1401 (or networks)
via the
antenna 1560.
In addition to processing communications signals, the DSP 1580 provides for
control of the receiver 1500 and the transmitter 1520. For example, gains
applied to
communications signals in the receiver 1500 and transmitter 1520 may be
adaptively
controlled through automatic gain control algorithms implemented in the DSP
1580.
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In a data communications mode, a received signal, such as a text message or
web
page download, is processed by the communications subsystem 1001 and is input
to the
processing device 1800. The received signal is then further processed by the
processing
device 1800 for an output to the display 1600, or alternatively to some other
auxiliary I/O
device 1060. A device user may also compose data items, such as e-mail
messages, using
the keypad 1400 and/or some other auxiliary I/O device 1060, such as a
touchpad, a rocker
switch, a thumb-wheel, or some other type of input device. The composed data
items may
then be transmitted over the communications network 1401 via the
communications
subsystem 1001.
In a voice communications mode, overall operation of the device is
substantially
similar to the data communications mode, except that received signals are
output to a
speaker 1100, and signals for transmission are generated by a microphone 1120.
Alternative voice or audio I/O subsystems, such as a voice message recording
subsystem,
may also be implemented on the device 1000. In addition, the display 1600 may
also be
utilized in voice communications mode, for example to display the identity of
a calling
party, the duration of a voice call, or other voice call related information.
The short-range communications subsystem enables communication between the
mobile device 1000 and other proximate systems or devices, which need not
necessarily
be similar devices. For example, the short-range conununications subsystem may
include
an infrared device and associated circuits and components, or a BluetoothTM
communications module to provide for communication with similarly-enabled
systems
and devices.
Many modifications and other embodiments of the invention will come to the
mind
of one skilled in the art having the benefit of the teachings presented in the
foregoing
descriptions and the associated drawings. Therefore, it is understood that the
invention is
not to be limited to the specific embodiments disclosed, and that
modifications and
embodiments are intended to be included within the scope of the invention.
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