U.S. patent application number 12/538446 was filed with the patent office on 2009-12-03 for joint maximal ratio combining using time-domauin based signal processing.
This patent application is currently assigned to IPR Licensing, Inc.. Invention is credited to Gary L. Sugar, Yohannes Tesfai, Chandra Vaidyanathan.
Application Number | 20090296848 12/538446 |
Document ID | / |
Family ID | 32045787 |
Filed Date | 2009-12-03 |
United States Patent
Application |
20090296848 |
Kind Code |
A1 |
Tesfai; Yohannes ; et
al. |
December 3, 2009 |
JOINT MAXIMAL RATIO COMBINING USING TIME-DOMAUIN BASED SIGNAL
PROCESSING
Abstract
A communication device transmits and receives communication
signals with another communication device via a plurality of
antennas, a plurality of transmit tapped delay-line filters, a
plurality of receive tapped delay-line filters, a combiner/analyzer
with a plurality of filters for signal processing; and a plurality
of computation blocks. The computation blocks determine complex
weights for the tapped delay-line filters for optimizing the
received signal-to-noise ratio and the range of communication of
the communication device.
Inventors: |
Tesfai; Yohannes; (Silver
Spring, MD) ; Vaidyanathan; Chandra; (Bethesda,
MD) ; Sugar; Gary L.; (Rockville, MD) |
Correspondence
Address: |
VOLPE AND KOENIG, P.C.;DEPT. ICC
UNITED PLAZA, SUITE 1600, 30 SOUTH 17TH STREET
PHILADELPHIA
PA
19103
US
|
Assignee: |
IPR Licensing, Inc.
Wilmington
DE
|
Family ID: |
32045787 |
Appl. No.: |
12/538446 |
Filed: |
August 10, 2009 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10707588 |
Dec 23, 2003 |
7573945 |
|
|
12538446 |
|
|
|
|
10064482 |
Jul 18, 2002 |
6873651 |
|
|
10707588 |
|
|
|
|
60380139 |
May 6, 2002 |
|
|
|
60361055 |
Mar 1, 2002 |
|
|
|
Current U.S.
Class: |
375/267 |
Current CPC
Class: |
H04B 7/0854 20130101;
H04L 1/06 20130101; H04B 7/0671 20130101; H04B 7/0845 20130101;
H04L 27/2601 20130101; H04L 2025/03445 20130101; H04B 7/0413
20130101; H04W 52/42 20130101; H04B 7/0615 20130101; H04B 7/0669
20130101; H04B 7/0857 20130101; H04L 25/0204 20130101 |
Class at
Publication: |
375/267 |
International
Class: |
H03K 5/159 20060101
H03K005/159; H04B 7/02 20060101 H04B007/02 |
Claims
1. A communication device, comprising: a plurality of transmit
tapped delay-line filters, each associated with a corresponding one
of a plurality of antennas; a plurality of receive tapped
delay-line filters, each associated with a corresponding one of the
plurality of antennas; a combiner/analyzer with a plurality of
filters for signal processing; and a plurality of computation
blocks that: generate a transmit filter vector for processing a
signal to be transmitted to another communication device, the
transmit filter vector comprised of a plurality of transmit filter
sub-vectors defining one or more complex weights associated with
the transmit tapped-delay line filter, each transmit filter
sub-vector associated with a corresponding one of the plurality of
antennas and having a length corresponding to the number taps of
the associated transmit tapped-delay line filter; apply the
transmit filter vector to a signal to be transmitted from the other
communication device; generate a receive filter matrix from a
signal received by the plurality of antennas from the other
communication device, the receive filter matrix comprised of a
plurality of sub-matrices each being a convolution matrix derived
from a receive filter sub-vector, wherein each receive filter
sub-vector defines one or more complex weights associated with the
receive tapped-delay line filter for a corresponding one of each of
the plurality of antennas; compute a principal eigenvector of a
product of the receive filter matrix and a Hermitian of the receive
filter matrix, the principal eigenvector comprised of a plurality
of sub-vectors each having a length corresponding to the number of
taps of the transmit tapped-delay line filter; and update from the
plurality of sub-vectors of the principal eigenvector the plurality
of transmit filter sub-vectors.
2. The communication device of claim 1, wherein the computation
blocks update the plurality of transmit sub-vectors by equating
each transmit filter sub-vector to the corresponding sub-vector of
the principal eigenvector.
3. The communication device of claim 1, wherein the computation
blocks update the plurality of transmit sub-vectors by computing
the norm of each of a plurality of sub-vectors of the principal
eigenvector and dividing each sub-vector of the principal
eigenvector by the norm and by the square root of the number of the
plurality of antennas so that the power of the signal transmitted
is divided equally among the plurality of antennas.
4. The communication device of claim 1, further comprising: a
plurality of antennas; a transmitter coupled to the plurality of
antennas and to the combiner/equalizer to upconvert transmit
signals generated by the combiner/equalizer for transmission via
respective ones of the plurality of antennas; and, a receiver
coupled to the plurality of antennas and to the combiner/equalizer
to downconvert signals received by the plurality of antennas and
produce receive signals that are coupled to the
combiner/equalizer.
5. The communication device of claim 1, wherein the
combiner/equalizer is a decision feedback equalizer comprising
feedforward filters, a decision block, and a feedback filter.
6. The communication device of claim 1, wherein the
combiner/equalizer comprises a maximum likelihood sequence
estimator used for maximum likelihood sequence estimator signal
processing.
7. The communication device of claim 1, wherein the
combiner/equalizer comprises a correlator used for direct sequence
spread spectrum signal processing.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 10/707,588 filed Dec. 23, 2003, which claims priority to U.S.
application Ser. No. 10/064,482 filed Jul. 18, 2002, which in turn
claims priority to U.S. Provisional Application No. 60/361,055,
filed Mar. 1, 2002, and to U.S. Provisional Application No.
60/380,139, filed May 6, 2002. The entirety of each of these prior
applications is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] The present invention is directed to a joint temporal and
spatial antenna processing scheme useful in wireless communication
applications, such as short-range wireless applications.
[0003] Antenna diversity schemes are well known techniques to
improve the performance of radio frequency (RF) communication
between two RF devices. Types of antenna diversity schemes include
antenna selection diversity and maximal ratio combining. In an
antenna selection diversity scheme, a radio communication device
selects one of N (e.g., two) antennas for transmission to a
particular communication device based on which of its N antennas
best received a signal from that radio communication device. On the
other hand, maximal ratio combining schemes involve scaling the
signal to be transmitted with a complex antenna weight associated
with a corresponding one of a plurality of antennas. A signal
received by a plurality of antennas can also be weighted by a
plurality of complex receive antenna weights. Selection of the
antenna weights to optimize communication between two communication
devices determines the performance of maximal ratio combining
schemes.
[0004] A joint maximal ratio combining antenna processing technique
is one in which a first communication device, having a plurality of
antennas, weights a signal to be transmitted by its antennas to a
second communication device also having a plurality of antennas.
The second communication device weights and combines the received
signals received by its antennas. The transmit weights and receive
weights are determined to optimize the link margin, e.g., optimize
the signal-to-noise ratio of signals received by one device from
the other. Techniques related to joint maximal ratio combining,
also called composite beamforming (CBF), are the subject matter of
above-identified commonly assigned co-pending application. These
techniques significantly extend the range of communication between
the two communication devices.
[0005] An approach is desired for a joint maximal ratio combining
technique that requires relatively low complexity computations be
performed in a communication device.
SUMMARY OF THE INVENTION
[0006] A communication device is configured to optimize the
received signal-to-noise ratio (SNR) based on the transmit filter
at another radio communication device. An iterative process is
provided to determine complex weights for tapped delay-line
transmit filters at each of two communication devices that optimize
the received SNR. When one communication device receives a signal
from another device, it generates a receive filter matrix from a
signal received by its one or more antennas. The receive filter
matrix is comprised of one or more sub-matrices each being a
convolution matrix derived from a receive filter sub-vector,
wherein each receive filter sub-vector defines one or more complex
weights associated with a receive tapped-delay line filter for the
one or more antennas. The receiving device computes the eigenvector
corresponding to the maximum eigenvalue of a product of the receive
filter matrix and a Hermitian of the receive filter matrix. This
eigenvector is the principal eigenvector of that matrix
multiplication product. The principal eigenvector is comprised of
one or more sub-vectors each having a length corresponding to a
number of taps of a transmit tapped-delay line filter associated
with the one or more antennas. From the one or more sub-vectors of
the principal eigenvector one or more transmit filter sub-vectors
that form a transmit filter vector are determined. Each transmit
filter sub-vector corresponds to the one or more antennas of the
second communication device and defining one or more complex
weights associated with the transmit tapped-delay line filter for
the one or more antennas of the second communication device. The
transmit filter vector is used by that device when transmitting a
signal back to the other device.
[0007] The two communication devices will ultimately converge to
transmit filter vectors that optimize the received SNR at the
output of the receive filters of the other device. The range of
communication, i.e., distance between the devices, is significantly
increased using the techniques described herein.
[0008] The above and other objects and advantages will become more
readily apparent when reference is made to the following
description taken in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a block diagram of two communication devices
performing time-domain based composite beamforming.
[0010] FIG. 2 is a diagram showing an exemplary channel matrix.
[0011] FIG. 3 is a diagram showing exemplary transmit filter
vectors.
[0012] FIG. 4 is a flow diagram illustrating an iterative process
for time-domain based composite beamforming between two
communication devices.
[0013] FIG. 5 is a graphical diagram showing exemplary results of
the time-domain based iterative process.
[0014] FIG. 6 is a graphical diagram showing performance loss for
ideal channel conditions.
[0015] FIG. 7 is a graphical diagram showing performance loss for
the iterative process.
[0016] FIG. 8 is a block diagram of a communication device suitable
for implementing the time-domain composite beamforming signal
processing.
DETAILED DESCRIPTION
[0017] Referring first to FIG. 1, two radio communication devices
100 and 200 are shown that communicate with each other across a
wireless transmission channel that can be defined by a channel
matrix H (or H.sup.T, where .sup.T denotes the transpose operator).
When the signal-to-noise ratio (SNR) at the output of the receive
filters of one device is optimized with respect to the transmit
filters at the other device, the ideal transmit filter vector
w.sub.T is given by the eigenvector corresponding to the maximum
eigenvalue e.sub.max of (H.sup.HH), which is the principal
eigenvector of (H.sup.HH), where H denotes the Hermitian operator.
This ideal case assumes that the devices have direct knowledge of
the channel, obtained from training sequences or from a separate
signal containing channel information that is transmitted from one
device to the other.
[0018] Described below is a system and method to optimize the SNR
at the output of the receive filters of one device (hereinafter
referred to as the received SNR) with respect to tapped delay-line
transmit filters at another device without direct knowledge of the
channel between the devices. Using an estimate of the channel
obtained from a signal received at one device from another device,
an iterative process is provided that determines the transmit
filters at the communication device at each iteration. The transmit
filters at each device converge to a received SNR that is within
1-2 dB of the ideal case SNR in about 2 to 3 iterations for more
than 90% of channel realizations.
[0019] With this background, communication device 100 includes,
among other components, a transmit shaping filter 110, a plurality
of transmit antenna filters 120(1)-120(N) and N plurality of
antennas 130(1) to 130(N). There is a transmit antenna filter
120(i) associated with a corresponding antenna 130(i), where i is
the antenna index, for i=1 to N. Each transmit antenna filter
120(i) is, for example, a tapped delay-line filter having a number
of taps. For each tap of the tapped delay-line filter, there is a
complex weight having a magnitude component and a phase component.
For example, a single tap delay-line filter has a single weight,
and therefore a flat or constant magnitude and a flat or constant
phase response across frequency. The characteristic of each
transmit filter 120 is defined by a transmit filter sub-vector
w.sup.i.sub.T,D1, and the length of the transmit filter sub-vector
corresponds to the number of taps of the transmit antenna filter
120(i). The entry in each sub-vector defines the corresponding tap
weight for the delay-line filter. The transmit filter sub-vectors
can be grouped together to form a transmit filter vector. The
filter vector and sub-vectors will be described further
hereinafter.
[0020] Similarly, for purposes of processing a received signal, the
communication device 100 comprises a plurality of receive antenna
filters 140(1) to 140(N) and a combiner/detector 150. There is a
receive antenna filter 140(i) coupled to an associated antenna
130(i). Each receive antenna filter 140(i) is, for example, a
tapped delay-line filter having a number of taps, and is
essentially a matched filter. A combiner/equalizer 150 is coupled
to the receive antenna filters 140(1) to 140(N). The characteristic
of each receive filter 140(i) is defined by a receive filter
sub-vector w.sup.i.sub.R,D1 having a length corresponding to the
number of taps of the receive antenna filters 140. The entry in
each receive filter sub-vector w.sup.i.sub.R,D1 defines the
corresponding complex tap weight for the delay-line filter. There
are computation elements or blocks represented by reference numeral
160 in communication device 100 that perform discrete signal
computations with respect to the transmit antenna filters 120 and
receive antenna filters 140 described hereinafter.
[0021] Communication device 200 includes components similar to
those in communication device 100. Communication device includes M
plurality of antennas 210(1) to 210(M), a plurality of receive
antenna filters 220(1) to 220(M) and a combiner/equalizer 230.
There is a receive antenna filter 220(j) (i.e., a matched filter)
associated with a corresponding antenna 210(j), where j is the
antenna index for j=1 to M. The characteristic of each receive
antenna filter 220(j) is defined by a receive filter sub-vector
w.sup.j.sub.R,D2. The receive filter sub-vectors can be grouped
together to form a receive filter vector. On the transmit side,
there is a transmit shaping filter 240 and a plurality of transmit
antenna filters 250(1) to 250(M) each associated with a
corresponding one of the antennas 210(1) to 210(M). The
characteristic of each transmit antenna filter 250(j) is defined by
a transmit filter sub-vector w.sup.j.sub.T,D2. Like communication
device 100, the receive antenna filters 220(j) and the transmit
antenna filters 250(j) are, for example, tapped delay-line filters
of a number of taps. The length of the receive filter sub-vectors
w.sup.j.sub.R,D2 correspond to the number of taps of the receive
antenna filters 220(j), and the length of the transmit filter
sub-vectors w.sup.j.sub.T,D2 correspond to the number of taps of
the transmit antenna filters 250(j). Communication device 200 has
computation elements or blocks represented by reference numeral 260
that perform discrete signal computations with respect to the
transmit antenna filters 250(j) and receive antenna filters 220(j)
described hereinafter. While FIG. 1 shows that communication
devices 100 and 200 each have a plurality of antennas, it should be
understood that one of them, for example, communication device 200,
may have a single antenna (and therefore a single transmit antenna
filter and a single receive antenna filter). In this case, only one
of the two devices of the communication link adapts its transmit
filter to optimize the receive SNR at the device with the single
antenna. The device with multiple antennas will adapt its receive
filter to optimize its receive SNR from the device with a single
antenna.
[0022] The communication device block diagram shown in FIG. 1 is
useful in a transceiver that processes signals of any wireless
communication modulation standard or format. Likewise, the methods
described herein are applicable to any wireless communication
modulation standard or format. An example is a code division
multiple access (CDMA) format using a single carrier. A more
specific example is the single-carrier scheme of the IEEE 802.11b
short-range wireless standard.
[0023] It should be understood to those of skill in the art that
FIG. 1 is a simplification of a communication device architecture
to highlight those components relevant to the composite beamforming
techniques described herein. For example, FIG. 1 omits (for the
sake of conciseness) digital-to-analog converters and a radio
frequency (RF) section between the antennas and the antenna
filters. FIG. 8, described hereinafter, is an example of a more
complete exemplary block diagram of a communication device. The
components shown in FIG. 1 are elements that typically are included
in a baseband section of a communication device and may be
implemented by discrete elements or by field programmable gate
arrays for digital signal processing integrated circuit
implementations. The combiner/equalizer 150 (and 230) is meant to
represent any suitable signal processing components used in a
receiver. For example, in the case of a decision feedback equalizer
(DFE), the combiner/equalizer block includes feedforward filters, a
decision block and a feedback filter. In the case of a maximum
likelihood sequence estimator (MLSE) receiver, there is a MLSE in
the combiner/equalizer block 160 (and 230), and in the case of a
direct sequence spread spectrum (DSSS) receiver, there is a
correlator in the combiner/equalizer block 160 (and 220).
[0024] When communication device 100 transmits a signal to
communication device 200, the communication channel between the N
plurality of antennas 130(1) to 130(N) and the M plurality of
antennas 210(1) to 210(M) is defined by a channel matrix H of
appropriate dimension as is shown in FIG. 2, described hereinafter.
Similarly, when communication device 200 transmits a signal to
communication device 100, the communication channel between the M
plurality of antennas 210(1) to 210(M) and the N plurality of
antennas 130(1) to 130(N) is defined by a channel matrix
H.sup.T.
[0025] Turning to FIG. 2, the channel matrix H is described in
further detail. The channel response from an antenna i of
communication device 100 to an antenna j of communication device
200 is defined by a channel response vector h.sup.ij and can be
modeled as a tapped delay-line filter having a length or number of
taps L. The channel response vector h.sup.ij can be written as
shown in FIG. 2. The channel response vector can also be written as
a convolution matrix H.sub.ij, where i is the index for the N
plurality of antennas of communication device 100 and j is the
index for the M plurality of antennas of communication device 200.
The dimensions of the channel response matrix H.sub.ij is
(L+LTD1-1).times.LTD1, where LTD1 is the length of the transmit
filters of the first communication device 100.
[0026] Referring back to FIG. 1, the transmit antenna filters
120(i) in communication device 100 have a length (i.e., number of
taps), and the transmit antenna filters 250(j) in communication
device 200 have a length. The lengths of the transmit antenna
filters 120(i) and 250(j) are not necessarily the same, and are
chosen according to implementation/performance requirements.
Obviously, the more taps a filter has, the greater performance it
will have, at the expense of implementation cost and complexity.
The length of the receive antenna filters 140(i) in communication
device 100 depends on the length of the transmit antenna filters
250(j) and the length of the channel response vector h.sup.ij
suitable for modeling the channel response between the first and
second communication devices. It can be shown that the length of
the receive antenna filters 140(i) (when receiving a signal from
communication device 200) is equal to the sum of the length of the
transmit antenna filters 250(j) plus the length of the channel
response vector h.sup.ij. Similarly, the length of the receive
antenna filters 220(j) (when receiving a signal from communication
device 100) is equal to the sum of the length of the transmit
antenna filters 120(i) plus the length of the channel response
vector h.sup.ij. The length of the transmit antenna filters 120(i)
and 250(j) do not depend on the length of the receive antenna
filters 140(i) and 220(j), respectively, and can be set to any
desired length. For example, it has been determined through
simulation that transmit antenna filters 120(i) and 250(j) can be
single tap delay-line filters and still achieve acceptable
performance.
[0027] With reference to FIG. 3, similar to the channel matrix H,
there is a transmit filter matrix W.sup.i.sub.T,D1 associated with
each antenna i of the first communication device 100 (for
transmitting a signal to the second communication device 200). A
transmit filter super matrix is matrix comprising a plurality of
sub-matrices corresponding to the transmit filter matrix associated
with each antenna i. The transmit filter matrix W.sup.i.sub.T,D1 is
a convolution matrix representation of the corresponding transmit
antenna filter sub-vector w.sup.i.sub.T,D1. The transmit antenna
filter vector w.sub.T,D1 is essentially a super-vector comprised of
a plurality of transmit filter sub-vectors w.sup.i.sub.T,D1, each
transmit filter sub-vector corresponding to or associated with a
transmit filter (FIG. 1), which in turn is associated with one of
the plurality of antennas of the first communication device 100.
For notation purposes, the length of each transmit filter
sub-vector of communication device 100 is LTD1, as described above
in conjunction with FIG. 2. Therefore, the length of the transmit
antenna filter vector is N*LTD1. The dimensions of each transmit
filter matrix is (LTD1+L-1).times.L, and the dimension of each
transmit filter sub-vector is LTD1.times.1.
[0028] Though not shown in FIG. 3, the transmit filter vector of
the second communication device 200 (for transmitting a signal to
the first communication device 100) is similarly defined as
w.sup.j.sub.T,D2, associated with each antenna j of the second
communication device 200. The length of the transmit antenna filter
sub-vector (and thus the number of taps of the transmit antenna
filters 250(j)) for the second communication device 200 is denoted
LTD2. The receive filter matrix for the first communication device
is denoted W.sub.R,D1, and the receive filter matrix for the second
communication device is denoted W.sub.R,D2. Each receive filter
matrix comprises a sub-matrix for each antenna of that device. Each
sub-matrix is a convolution matrix derived from the receive filter
sub-vector associated with the corresponding antenna depicted in
FIG. 3. The filter length LTD1 of the first communication device
and the filter length of LTD2 of the second communication device
need not be the same.
[0029] FIG. 4 illustrates the iterative process 400. The process
400 is described with respect to communication between the first
communication device (denoted by the index D1) having N antennas
and the second communication device (denoted by the index D2)
having M antennas. It is to be understood that the process is
applicable to any wireless communication application, such as, for
example, a short-range wireless application. The process begins in
step 410 when the first communication device transmits a signal to
the second communication device using an initial transmit filter
vector w.sub.T,D1,0=[10 . . . 0,10 . . . 0,10 . . . 0,10 . . .
0].sup.T according to the notation shown, normalized by the factor
1/(N).sup.1/2 (1 divided by the square root of N), where N is the
number of antennas of the first communication device. The purpose
of this factor will be described hereinafter. The initial transmit
filter vector has a unity value at the initial position in each
sub-vector for each antenna. In step 410, the first communication
device transmits a signal with the initial transmit filter vector
to the second communication device. The second communication device
receives the transmitted signal, and from the received signal, the
second communication device estimates a vector corresponding to the
product W.sub.R,D2,0e.sub.0, where e.sub.0 is the vector [10 . . .
0].sup.T. From this quantity, the second communication device
obtains the initial receive filter vector w.sub.R,D2,0 and builds
the receive filter matrix W.sub.R,D2,0 with dimensions that, after
further computations, will result in a transmit filter vector
w.sub.T,D2 that has the desired filter length.
[0030] In step 420, the second communication device computes a
principal eigenvector u.sub.T,D2 which is the eigenvector
corresponding to the maximum eigenvalue of the product of
{(W.sub.R,D2,0).sup.H W.sub.R,D2,0}. The principal eigenvector
u.sub.T,D2,0 has a length of M*LTD2. The principal eigenvector
u.sub.T,D2,0 is a super vector, or vector of sub-vectors, where
each sub-vector u.sup.j.sub.T,D2,0 has a length LTD2 and is used to
derive the transmit antenna filter sub-vector w.sup.j.sub.T,D2,0
for a corresponding antenna of the second communication device.
[0031] Each transmit filter sub-vector w.sup.j.sub.T,D2 can take on
one of two values as shown in FIG. 4. In one case, the transmit
filter sub-vector w.sup.j.sub.T,D2 is equal to the corresponding
sub-vector u.sup.j.sub.T,D2,0 of the principal eigenvector
u.sub.T,D20. This is called the non-equal gain case indicated
"NON-EG" in FIG. 4. In another case, the transmit antenna
sub-vector w.sup.j.sub.T,D2 is equal to the corresponding
sub-vector u.sup.j.sub.T,D2,0 of the principal eigenvector
u.sub.T,D2,0 divided by the norm of that sub-vector
u.sup.j.sub.T,D2,0 and by (M).sup.1/2 (square root of M). This case
is called the equal-gain case, indicated "EG" in FIG. 4. This
further computation equal-gain normalizes the magnitude of each
filter sub-vector so that the power of the signal transmitted at
each antenna using the transmit filter sub-vectors is equal. This
equal gain constraint is advantageous because it has been found to
yield performance very close to non-equal gain antenna processing
(within 1-2 dB), but substantially reduces the power output
requirements for each power amplifier associated with each antenna.
The advantages of equal-gain composite beamforming are further
described in the aforementioned co-pending application entitled
"System and Method for Antenna Diversity Using Equal Gain Joint
Maximal Ratio Combining." The initial transmit filter sub-vectors
used by the first communication device in step 410 can optionally
be equal gain normalized, as indicated in FIG. 4 with the
(N).sup.1/2 factor.
[0032] In step 420, the first communication device receives the
signal transmitted by the second communication device using the
transmit sub-vectors w.sup.j.sub.T,D2 and estimates a vector
corresponding to the product W.sub.R,D1,0e.sub.0 to obtain the
initial receive filter vector w.sub.R,D1,0. At the next iteration
in step 430, the first communication device performs a process
similar to the one performed by the second communication device in
step 420, to compute the principal eigenvector u.sub.T,D1,1 and
generate therefrom updated transmit filter sub-vectors for each
antenna of the first communication device using either the equal
gain computation or non-equal gain relationship.
[0033] Steps 440 and 450 show that this process repeats and the
transmit filter sub-vectors at the first and second communication
devices converge to values that optimize the received SNR at each
of them. The transmit filter sub-vectors computed at each iteration
are stored. Even though the transmit filter sub-vectors will
ultimately converge after several iterations, they can be
continuously updated with each transmission between those
communication devices beyond convergence. In addition, it may also
be desirable to store the most recent or updated transmit filter
sub-vectors in one communication device against and identifier of
the particular destination communication device. In this way, when
a subsequent communication session is initiated between those same
communication devices, each device can retrieve the stored transmit
filter sub-vectors for use in transmitting signals to the other
device.
[0034] Optimizing the transmit filters at the first and second
communication device in this way significantly increases the range
(i.e., distance) between the devices. This can be very advantageous
in a wireless communication environments, such as short-range
wireless applications. A wireless LAN is one example of a
short-range wireless application.
[0035] The computations referred to in the description of the
iterative process 400 may be performed by the discrete signal
computation blocks 160 and 260 (using digital signal processing
techniques), respectively, in communication devices 100 and 200.
For example, when communication device 100 or 200 receives a signal
from the other device, there is a channel estimator computation
block that estimates the composite channel transmit filter response
of the transmitting communication device in order to determine the
receive super matrix W.sub.R. There is a computation block that
forms the receive vector W.sub.R and from that vector builds the
receive convolution matrix W.sub.R for each antenna. There are also
one or more computation blocks that compute super matrix W.sub.R,
the Hermitian of the receive super matrix W.sub.R, multiply it with
the receive matrix W.sub.R and compute the principle or principal
eigenvector of the matrix product of that matrix multiplication.
Computation blocks are also provided that normalize each sub-vector
(divide by the norm of the principal eigenvectors and by the
square-root of the number of antennas) for each antenna sub-vector.
Moreover, the transmit antenna filters and receive antenna filters
in each communication device may similarly be implemented by
computational blocks.
[0036] FIGS. 5 to 7 show various performance metrics of the
iterative scheme. FIG. 5 shows the loss in SNR of the iterative
scheme of FIG. 4 relative to the case where the channel state is
known at the transmitting device (hereinafter called the "ideal
case"). The loss in SNR due to the iterative scheme is less than 2
dB for more than 90% of channel realizations.
[0037] FIG. 6 shows loss in SNR using the equal gain constraint in
the ideal case relative to the non-equal gain ideal case. That is,
both communication devices, when transmitting to the other device,
constrain the power of the signal at each antenna to be equal. The
loss in SNR for the equal gain case is less than 1 dB for more than
90% of channel realizations.
[0038] FIG. 7 shows the loss in SNR using the equal gain constraint
in the iterative scheme of FIG. 4. The loss in SNR using equal gain
is less than 1 dB for more than 90% of channel realizations.
[0039] FIG. 8 shows a more complete exemplary block diagram of a
communication device useful in accordance with the techniques
described herein. The communication devices at both ends of the
link, i.e., devices 100 and 200 may have any known suitable
architecture to transmit, receive and process signals. An example
of a communication device block diagram is shown in FIG. 8. The
communication device comprises an RF section 310, a baseband
section 320 and optionally a host 330. There are a plurality of
antennas, e.g., four antennas 302, 304, 306, 308 coupled to the RF
section 310 that are used for transmission and reception. The RF
section 310 has a transmitter (Tx) 312 that upconverts baseband
signals for transmission, and a receiver (Rx) 314 that downconverts
received RF signals for baseband processing. In the context of the
composite beamforming techniques described herein, the Tx 312
upconverts and supplies separately weighted signals to
corresponding ones of each of the plurality of antennas via
separate power amplifiers. Similarly, the Rx 314 downconverts and
supplies received signals from each of the plurality of antennas to
the baseband section 320. The baseband section 320 performs
processing of baseband signals to recover the information from a
received signal, and to convert information in preparation for
transmission. The baseband section 320 may implement any of a
variety of communication formats or standards, such as WLAN
standards IEEE 802.11x, frequency hopping standards such as
Bluetooth.TM., as well as other protocol standards, not necessarily
used in a WLAN. In the case of frequency hopping systems, the
antenna sub-vectors are computed and stored for each frequency in a
frequency hopping sequence.
[0040] The intelligence to execute the computations for the
composite beamforming techniques described herein may be
implemented in a variety of ways. For example, a processor 322 in
the baseband section 320 may execute instructions encoded on a
processor readable memory 324 (RAM, ROM, EEPROM, etc.) that cause
the processor 322 to perform the composite beamforming steps
described herein. Alternatively, as suggested above, an application
specific integrated circuit (ASIC) may be fabricated with the
appropriate firmware e.g., field programmable gate arrays (FPGAs),
configured to execute the computations described herein. This ASIC
may be part of, or the entirety of, the baseband section 320. For
example, the components shown in FIG. 1 as part of the
communication devices may be implemented by FPGAs in the baseband
section 320. Still another alternative is for the beamforming
computations to be performed by a host processor 332 (in the host
330) by executing instructions stored in (or encoded on) a
processor readable memory 334. The RF section 310 may be embodied
by one integrated circuit, and the baseband section 320 may be
embodied by another integrated circuit. The communication device on
each end of the communication link need not have the same device
architecture or implementation.
[0041] To summarize, a method is provided for communicating signals
between a first communication device and a second communication
device using radio frequency (RF) communication techniques. At the
first communication device there are steps of generating a transmit
filter vector for processing a signal to be transmitted from the
first communication device to the second communication device, the
transmit filter vector comprised of a plurality of transmit filter
sub-vectors defining one or more complex weights associated with a
transmit tapped-delay line filter, each transmit filter sub-vector
associated with a corresponding one of a plurality of antennas of
the first communication device and having a length corresponding to
the number taps of the associated transmit tapped-delay line
filter; and applying the transmit filter vector to a signal to be
transmitted from the first communication device to the second
communication device.
[0042] At the second communication device there are steps of
generating a receive filter matrix from a signal received by the
one or more antennas of the second communication device from the
first communication device, the receive filter matrix comprised of
one or more sub-matrices each being a convolution matrix derived
from a receive filter sub-vector, wherein each receive filter
sub-vector defines one or more complex weights associated with a
receive tapped-delay line filter for the one or more antennas of
the second communication device; computing a principal eigenvector
of a product of the receive filter matrix and a Hermitian of the
receive filter matrix, the principal eigenvector comprised of one
or more sub-vectors each having a length corresponding to a number
of taps of a transmit tapped-delay line filter associated with the
one or more antennas of the second communication device; deriving
from the one or more sub-vectors of the principal eigenvector one
or more transmit filter sub-vectors that form a transmit filter
vector, each transmit filter sub-vector corresponding to the one or
more antennas of the second communication device and defining one
or more complex weights associated with the transmit tapped-delay
line filter for the one or more antennas of the second
communication device; and applying the transmit filter vector at
the second communication device to a signal to be transmitted from
the second communication device to the first communication device.
The transmit filter vector at either or both of the first and
second communication devices may be normalized at each sub-vector
so that the total power associated with a transmitted signal is
divided equally among the plurality of antennas of the first
communication device.
[0043] Similarly, a method is provided for radio communication
between a first communication device and a second communication
device, comprising steps of generating a transmit filter vector for
processing a signal to be transmitted from the first communication
device to the second communication device, the transmit filter
vector comprised of a plurality of transmit filter sub-vectors
defining one or more complex weights associated with a transmit
tapped-delay line filter, each transmit filter sub-vector
associated with a corresponding one of a plurality of antennas of
the first communication device and having a length corresponding to
the number taps of the associated transmit tapped-delay line
filter; applying the transmit filter vector to a signal to be
transmitted from the first communication device to the second
communication device; generating a receive filter matrix from a
signal received by the plurality of antennas of the first
communication device from the second communication device, the
receive filter matrix comprised of a plurality of sub-matrices each
being a convolution matrix derived from a receive filter
sub-vector, wherein each receive filter sub-vector defines one or
more complex weights associated with a receive tapped-delay line
filter process for the each of the plurality of antennas of the
first communication device; computing a principal eigenvector of a
product of the receive filter matrix and a Hermitian of the receive
filter matrix, the principal eigenvector comprised of a plurality
of sub-vectors each having a length corresponding to the number of
taps of the transmit tapped-delay line filter process of the first
communication device; and updating from the plurality of
sub-vectors of the principal eigenvector the plurality of transmit
filter sub-vectors. This method may be implemented by instructions
encoded on a medium, such as a processor readable medium, or by
instructions implemented by one or more arrays of field
programmable gates.
[0044] Further still, a semiconductor device is provided comprising
a plurality of field programmable gates configured to implement: a
plurality of transmit tapped delay-line filters, each associated
with a corresponding one of a plurality of antennas; a plurality of
receive tapped delay-line filters, each associated with a
corresponding one of the plurality of antennas; and one or more
computation blocks that generate a transmit filter vector for
processing a signal to be transmitted to another communication
device, the transmit filter vector comprised of a plurality of
transmit filter sub-vectors defining one or more complex weights
associated with the transmit tapped-delay line filter, each
transmit filter sub-vector associated with a corresponding one of
the plurality of antennas and having a length corresponding to the
number taps of the associated transmit tapped-delay line filter;
apply the transmit filter vector to a signal to be transmitted from
the other communication device; generate a receive filter matrix
from a signal received by the plurality of antennas from the other
communication device, the receive filter matrix comprised of a
plurality of sub-matrices each being a convolution matrix derived
from a receive filter sub-vector, wherein each receive filter
sub-vector defines one or more complex weights associated with a
receive tapped-delay line filter process for the each of the
plurality of antennas; compute a principal eigenvector of a product
of the receive filter matrix and a Hermitian of the receive filter
matrix, the principal eigenvector comprised of a plurality of
sub-vectors each having a length corresponding to the number of
taps of the transmit tapped-delay line filter process; and update
from the plurality of sub-vectors of the principal eigenvector the
plurality of transmit filter sub-vectors. The semiconductor device
may be, for example, an digital application specific integrated
circuit implemented using field programmable gate arrays or digital
logic implementations, such as CMOS digital logic.
[0045] The above description is intended by way of example
only.
* * * * *