U.S. patent application number 12/706042 was filed with the patent office on 2010-06-17 for method and system for minimizing effects of transmitter impairments in multiple input multiple output (mimo) beamforming communication systems.
Invention is credited to Sirikiat Lck Ariyavisitakul, Joonsuk Kim, Eric Ojard.
Application Number | 20100150260 12/706042 |
Document ID | / |
Family ID | 38443968 |
Filed Date | 2010-06-17 |
United States Patent
Application |
20100150260 |
Kind Code |
A1 |
Ariyavisitakul; Sirikiat Lck ;
et al. |
June 17, 2010 |
METHOD AND SYSTEM FOR MINIMIZING EFFECTS OF TRANSMITTER IMPAIRMENTS
IN MULTIPLE INPUT MULTIPLE OUTPUT (MIMO) BEAMFORMING COMMUNICATION
SYSTEMS
Abstract
Aspects of a method and system for minimizing effects of
transmitter impairments in multiple input multiple output (MIMO)
beamforming communication systems are presented. In one aspect of a
system for minimizing effects of transmitter impairments, a MIMO
transmitter may enable nulling of transmitter-induced noise by
adjusting at least a portion of a plurality of signals transmitted
based on a transmitter error vector magnitude (EVM). The
transmitter may enable transmission of the plurality of signals
subsequent to the nulling. In another aspect of a system for
minimizing effects of transmitter impairments a MIMO receiver may
enable nulling of transmitter-induced noise contained in a
plurality of received signals based on a transmitter EVM. Each of
the plurality of received signals may include information contained
in a plurality of spatial streams. The receiver may enable
detecting estimated values for the information contained in the
plurality of spatial streams based on the nulling.
Inventors: |
Ariyavisitakul; Sirikiat Lck;
(Alpharetta, GA) ; Ojard; Eric; (San Francisco,
CA) ; Kim; Joonsuk; (San Jose, CA) |
Correspondence
Address: |
MCANDREWS HELD & MALLOY, LTD
500 WEST MADISON STREET, SUITE 3400
CHICAGO
IL
60661
US
|
Family ID: |
38443968 |
Appl. No.: |
12/706042 |
Filed: |
February 16, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11449413 |
Jun 8, 2006 |
7664200 |
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12706042 |
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60776523 |
Feb 24, 2006 |
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Current U.S.
Class: |
375/267 |
Current CPC
Class: |
H04L 1/0631
20130101 |
Class at
Publication: |
375/267 |
International
Class: |
H04B 7/02 20060101
H04B007/02 |
Claims
1-22. (canceled)
23. A method for processing signals in a communication system, the
method comprising: performing by one or more processors and/or
circuits: nulling, in a multiple input multiple output (MIMO)
communication system, transmitter-induced noise contained in a
plurality of received signals at a receiver based on a transmitter
error vector magnitude wherein each of said plurality of received
signals comprises information contained in a plurality of spatial
streams; and detecting estimated values for said information
contained in said plurality of spatial streams based on said
nulling.
24. The method according to claim 23, further comprising computing
one or more of, an upper triangular matrix, a lower triangular
matrix, and a diagonal matrix, based on a channel estimate matrix,
a channel noise matrix, and said transmitter error vector
magnitude.
25. The method according to claim 24, wherein said channel estimate
matrix comprises a plurality of channel estimates values associated
with an RF channel utilized for receiving said plurality of
received signals.
26. The method according to claim 25, wherein said channel noise
matrix comprises values for channel noise associated with said RF
channel.
27. The method according to claim 24, further comprising computing
weights utilized for said detecting said estimated values based on
said one or more of, said upper triangular matrix, said lower
triangular matrix, and said diagonal matrix.
28. A method for processing signals in a communication system, the
method comprising: performing by one or more processors and/or
circuits: nulling, in a MIMO communication system,
transmitter-induced noise by adjusting at least a portion of a
plurality of signals transmitted based on a transmitter error
vector magnitude; and transmitting said plurality of signals
subsequent to said nulling.
29. The method according to claim 28, further comprising computing
a modified channel estimate matrix based on at least one or more
of, a channel estimate matrix, a channel noise matrix, a channel
noise value based on said channel noise matrix, and said
transmitter error vector magnitude.
30. The method according to claim 29, further comprising computing
beamforming weights utilized for said adjusting said at least a
portion of said plurality of signals based on said modified channel
estimate matrix.
31. The method according to claim 30, further comprising combining
a plurality of spatial streams based on said beamforming weights to
generate said plurality of signals.
32. The method according to claim 31, wherein a signal to noise
ratio for one of said plurality of spatial streams is approximately
equal to a corresponding signal to noise ratio for each remaining
one of said plurality of spatial streams.
33. The method according to claim 32, wherein a modulation type of
said one of said plurality of spatial streams is also utilized for
said each remaining one of said plurality of spatial streams.
34. The method according to claim 31, wherein a signal to noise
ratio for one of said plurality of spatial streams differs from a
corresponding signal to noise ratio for at least one of a remainder
of said plurality of spatial streams.
35. The method according to claim 34, wherein a modulation type of
said one of said plurality of spatial streams is a different
modulation type from a corresponding modulation type utilized for
said at least one of said remainder of said plurality of spatial
streams.
36. A system for processing signals in a communication system, the
system comprising: one or more circuits, in a MIMO communication
system, that enable nulling of transmitter-induced noise by
adjusting at least a portion of a plurality of signals transmitted
based on a transmitter error vector magnitude; and said one or more
circuits enable transmission of said plurality of signals
subsequent to said nulling.
37. The system according to claim 36, wherein said one or more
circuits enable computation of a modified channel estimate matrix
based on one or more of, a channel estimate matrix, a channel noise
matrix, a channel noise value based on said channel noise matrix,
and said transmitter error vector magnitude.
38. A system for processing signals in a communication system, the
system comprising: one or more circuits, in a MIMO communication
system, that enable computation of an augmented channel estimate
matrix based on a channel estimate matrix, a channel noise matrix,
and a transmitter error vector magnitude; said one or more circuits
enable computation of a feedback vector based on said augmented
channel estimate matrix; and said one or more circuits enable
nulling of transmitter induced noise by adjusting at least a
portion of signal levels for a plurality of input signals based on
said feedback vector.
39. A system for processing signals in a communication system, the
system comprising: one or more circuits that enable nulling, in a
multiple input multiple output (MIMO) communication system, of
transmitter-induced noise contained in a plurality of received
signals at a receiver based on a transmitter error vector magnitude
wherein each of said plurality of received signals comprises
information contained in a plurality of spatial streams; and said
one or more circuits enable detection of estimated values for said
information contained in said plurality of spatial streams based on
said nulling.
40. The system according to claim 39, wherein said one or more
circuits enable computation of one or more of, an upper triangular
matrix, a lower triangular matrix, and a diagonal matrix, based on
a channel estimate matrix, a channel noise matrix and said
transmitter error vector magnitude.
41. The system according to claim 40, wherein said channel estimate
matrix comprises a plurality of channel estimates values associated
with an RF channel utilized for receiving said plurality of
received signals.
42. The system according to claim 41, wherein said channel noise
matrix comprises values for channel noise associated with said RF
channel.
43. The system according to claim 40, wherein said one or more
circuits enable computation of weights utilized for said detecting
said estimated values based on said one or more of, said upper
triangular matrix, said lower triangular matrix, and said diagonal
matrix.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY
REFERENCE
[0001] This application makes reference to, claims priority to, and
claims the benefit of U.S. Provisional Application Ser. No.
60/776,523 filed Feb. 24, 2006.
[0002] This application makes reference to:
U.S. patent application Ser. No. 11/417,688 filed on May 4,
2006.
[0003] Each of the above stated applications is hereby incorporated
herein by reference in its entirety.
FIELD OF THE INVENTION
[0004] Certain embodiments of the invention relate to wireless
communication. More specifically, certain embodiments of the
invention relate to a method and system for minimizing effects of
transmitter impairments in multiple input multiple output (MIMO)
beamforming communication systems.
BACKGROUND OF THE INVENTION
[0005] In multiple input multiple output (MIMO) wireless systems,
multiple data streams may be transmitted simultaneously using a
plurality of transmitting antennas. A MIMO receiver may utilize a
plurality of receiving antennas to decouple, and detect individual
data streams. Two predominant methods for MIMO transmission include
singular value decomposition (SVD), and layers space-time (LST)
processing, also known as successive interference cancellation
(SIC).
[0006] SVD may use beamforming in conjunction with a transmitter
antenna array and receiver antenna array to create virtual
channels, or eigen-channels, through which multiple data streams
may be sent without interfering with one another. LST/SIC may use
receiver antenna array processing to detect the multiple data
streams, one stream at a time. For each detection "layer," the
interference from yet undetected streams may be nulled out, while
the interference from already detected streams may be cancelled, or
subtracted, out.
[0007] The eigen-channels in SVD may have highly unequal signal to
noise ratios (SNR), depending on the eigen-spread of the MIMO
channel matrix. SVD may also rely upon adaptive modulation, or
adaptive bit loading, to achieve greater data transfer rates for
eigen-channels associated with higher SNR values, while
simultaneously supporting lower data transfer rates for
eigen-channels associated with lower SNR values. SVD may also
suffer performance loss, by not achieving the peak theoretical data
transfer rate aggregated among the eigen-channels when a broad
range of modulation types are not available. For example, if a
maximum data transfer rate associated with an eigen-channel
requires a 1024 QAM modulation type, the maximum data transfer rate
may not be achievable if the required modulation type is not
available to be utilized.
[0008] LST/SIC approaches may suffer performance losses as a result
of error propagation. For example, if a current layer is detected
in error, the error may propagate to other layers increasing the
probability that subsequent layers may also be detected in error.
LST/SIC may require stream re-ordering to detect data streams with
higher SNR values first to minimize error propagation. Some
methods, such as vertical LST (VLST) may provide error protection
through coding of each data stream. Based on the coding, decisions
may be made subsequent to decoding to subtract out
interference.
[0009] Alternatively, precoding based on dirty paper theory, for
example Tomlinson-Harashima precoding (THP), may be utilized to
pre-cancel interference at the transmitter without requiring the
signals to be transmitted with greater levels of transmitted
radiated power. The THP approach may require channel knowledge at
the transmitter.
[0010] Geometric mean decomposition (GMD) may utilize beamforming
and LST/SIC at transmitter, by utilizing THP for example, or at a
receiver, by utilizing VLST for example. SNRs for each of a
transmitted plurality of data streams may be about equal when
utilizing GMD. Consequently, adaptive bit loading may not be
required as may be the case with SVD. GMD may also not require
reordering of data streams as may be the case with LST/SIC. GMD may
achieve data transfer rates that are approximately equal to the
channel capacity.
[0011] Circuitry within a MIMO transmitter may cause noise to be
incorporated in transmitted signals. The noise may be referred to
as "transmitter-induced noise." A potential cause of
transmitter-induced noise may include nonlinearity in the output
signal gain of power amplifiers as a function of input signals.
Another potential cause of transmitter-induced noise may be phase
errors between corresponding in-phase (I) and quadrature phase (Q)
signals generated by the MIMO transmitter. These phase errors may
be referred to as "phase noise." In general, an error in a
magnitude and/or phase for an I signal, and/or an error in a
magnitude and/or phase for a corresponding Q signal may be referred
to as an "IQ imbalance". IQ imbalance is also a potential a cause
of transmitter-induced noise. A measure of transmitter-induced
noise is an error vector magnitude (EVM) as defined in IEEE
resolution 802.11n, for example.
[0012] Some conventional MIMO receivers may attempt to decode
information contained in received signals without compensating for
transmitter-induced noise. A result may be errors that occur during
the decoding of the information. Measures of errors at a MIMO
receiver during decoding may include bit error rate (BER) and
packet error rate (PER).
[0013] Techniques utilized in some conventional MIMO receivers
based on LST/SIC may require stringent limitations on transmitter
EVM to allow the MIMO receiver to achieve acceptable BER and/or PER
rates when receiving and/or decoding received signals. Meeting
these stringent limitations may require the utilization of
expensive circuitry in the MIMO transmitter.
[0014] Further limitations and disadvantages of conventional and
traditional approaches will become apparent to one of skill in the
art, through comparison of such systems with some aspects of the
present invention as set forth in the remainder of the present
application with reference to the drawings.
BRIEF SUMMARY OF THE INVENTION
[0015] A system and/or method for minimizing effects of transmitter
impairments in multiple input multiple output (MIMO) beamforming
communication systems, substantially as shown in and/or described
in connection with at least one of the figures, as set forth more
completely in the claims.
[0016] These and other advantages, aspects and novel features of
the present invention, as well as details of an illustrated
embodiment thereof, will be more fully understood from the
following description and drawings.
BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS
[0017] FIG. 1 is a block diagram of an exemplary system for
wireless data communications, which may be utilized in connection
with an embodiment of the invention.
[0018] FIG. 2 is a block diagram of an exemplary MIMO system that
may be utilized in connection with an embodiment of the
invention.
[0019] FIG. 3 is an exemplary diagram illustrating beamforming that
may be utilized in connection with an embodiment of the
invention.
[0020] FIG. 4 is an exemplary figure illustrating packet error rate
performance, in accordance with an embodiment of the invention.
[0021] FIG. 5 is a flowchart illustrating exemplary steps for
nulling at a MIMO receiver without GMD optimization, in accordance
with an embodiment of the invention.
[0022] FIG. 6 is a flowchart illustrating exemplary steps for
nulling at a MIMO transmitter with GMD optimization, in accordance
with an embodiment of the invention.
[0023] FIG. 7 is a block diagram for an exemplary system for
minimizing effects of transmitter impairments based on
Tomlinson-Harashima precoding (THP) using GMD, in accordance with
an embodiment of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0024] Certain embodiments of the invention relate to a method and
system for minimizing effects of transmitter impairments in
multiple input multiple output (MIMO) beamforming communication
systems. In various embodiments of the invention, a MIMO receiver
may utilize transmitter EVM values to compensate, or null,
transmitter-induced noise while decoding information contained in a
plurality of signals received from a MIMO transmitter.
[0025] In various embodiments of the invention, a MIMO transmitter
may modify beamforming parameters, utilized for transmitting a
plurality of signals, based on transmitter EVM values. The modified
beamforming parameters may enable the MIMO transmitter to
compensate for transmitter-induced noise while encoding and
transmitting information. The information may be transmitted via a
wireless communication medium, for example. In an exemplary
embodiment of the invention, the nulling may be referred to as
beamforming optimization. In such exemplary embodiments, the
nulling may be performed while maintaining intended relative SNR
levels for a transmitted plurality of signals. When signals
generated by the MIMO transmitter are based on geometric mean
decomposition (GMD), or uniform channel decomposition (UCD)
methods, for example, beamforming optimization may enable relative
SNR levels for each of the transmitted plurality of signals to be
about equal.
[0026] FIG. 1 is a block diagram of an exemplary system for
wireless data communications, which may be utilized in connection
with an embodiment of the invention. With reference to FIG. 1,
there is shown a distribution system (DS) 110, an extended service
set (ESS) 120, and an IEEE 802.x LAN 122. The ESS 120 may comprise
a first basic service set (BSS) 102, and a second BSS 112. The
first BSS 102 may comprise a first 802.11 WLAN station 104, a
second 802.11 WLAN station 106, and an access point (AP) 108. The
second BSS 112 may comprise a first 802.11 WLAN station 114, a
second 802.11 WLAN station 116, and an access point (AP) 118. The
IEEE 802 LAN 122 may comprise a LAN station 124, and a portal 126.
An IEEE 802.11 WLAN station, or IEEE 802.11 WLAN device, is a WLAN
system that may be compliant with at least a portion of the IEEE
802.11 standard.
[0027] A WLAN is a communications networking environment that
comprises a plurality of WLAN devices that may communicate
wirelessly via one or more uplink and/or downlink RF channels. The
BSS 102 or 112 may be part of an IEEE 802.11 WLAN that comprises at
least 2 IEEE 802.11 WLAN stations, for example, the first 802.11
WLAN station 104, the second 802.11 WLAN station 106, and the AP
108, which may be members of the BSS 102. Non-AP stations within
BSS 102, the first 802.11 WLAN station 104, and the second 802.11
WLAN station 106, may individually form an association with the AP
108. An AP, such as AP 108, may be implemented as an Ethernet
switch, bridge, or other device in a WLAN, for example. Similarly,
non-AP stations within BSS 112, the first 802.11 WLAN station 114,
and the second 802.11 WLAN station 116, may individually form an
association with the AP 118. Once an association has been formed
between a first 802.11 WLAN station 104 and an AP 108, the AP 108
may communicate reachability information about the first 802.11
WLAN station 104 to other APs associated with the ESS 120, such as
AP 118, and portals such as the portal 126. The WLAN station 104
may subsequently communicate information wirelessly via the BSS
102. In turn, the AP 118 may communicate reachability information
about the first 802.11 WLAN station 104 to stations in BSS 112. The
portal 126, which may be implemented as, for example, an Ethernet
switch or other device in a LAN, may communicate reachability
information about the first 802.11 WLAN station 104 to stations in
LAN 122 such as the 802 LAN station 124. The communication of
reachability information about the first 802.11 WLAN station 104
may enable WLAN stations that are not in BSS 102, but are
associated with ESS 120, to communicate wirelessly with the first
802.11 WLAN station 104.
[0028] The DS 110 may provide an infrastructure which enables a
first 802.11 WLAN station 104 in one BSS 102, to communicate
wirelessly with a first 802.11 WLAN station 114 in another BSS 112.
The DS 110 may also enable a first 802.11 WLAN station 104 in one
BSS 102 to communicate with an 802 LAN station 124 in an IEEE 802
LAN 122, implemented as, for example a wired LAN. The AP 108, AP
118, or portal 126 may provide a means by which a station in a BSS
102, BSS 112, or LAN 122 may communicate information via the DS
110. The first 802.11 WLAN station 104 in BSS 102 may communicate
information wirelessly to a first 802.11 WLAN station 114 in BSS
112 by transmitting the information wirelessly to AP 108, which may
transmit the information via the DS 110 to AP 118, which in turn
may transmit the information wirelessly to station 114 in BSS 112.
The first 802.11 WLAN station 104 may communicate information
wirelessly to the 802 LAN station 124 in LAN 122 by transmitting
the information wirelessly to AP 108, which may transmit the
information via the DS 110 to the portal 126, which in turn may
transmit the information to the 802 LAN station 124 in LAN 122. The
DS 110 may utilize wireless communications via an RF channel, wired
communications, such as IEEE 802 Ethernet, or a combination
thereof.
[0029] A WLAN station or AP may utilize one or more transmitting
antennas, and one or more receiving antennas when communicating
information. A WLAN station or AP that utilizes a plurality of
transmitting antennas and/or a plurality of receiving antennas may
be referred to as a multiple input multiple output (MIMO)
system.
[0030] FIG. 2 is a block diagram of an exemplary MIMO system that
may be utilized in connection with an embodiment of the invention.
With reference to FIG. 2 there is shown a memory 272, a transceiver
274, an RF front end 280, a plurality of receiving antennas 276a, .
. . , 276n, and a plurality of transmitting antennas 278a, . . . ,
278n. The transceiver 274 may comprise a processor 282, a receiver
284, and a transmitter 286.
[0031] The processor 282 may perform digital receiver and/or
transmitter functions in accordance with applicable communications
standards. These functions may comprise, but are not limited to,
tasks performed at lower layers in a relevant protocol reference
model. These tasks may further comprise the physical layer
convergence procedure (PLCP), physical medium dependent (PMD)
functions, and associated layer management functions. These
functions may comprise, but are not limited to, tasks related to
analysis of data received via the receiver 284, and tasks related
to generating data to be transmitted via the transmitter 286. These
tasks may further comprise medium access control (MAC) layer
functions as specified by pertinent standards. The memory 272 may
be utilized to store data and/or code, and/or utilized to retrieve
data and/or code. The memory 272 may receive data and/or code via
input signals along with input control signals that enable the
memory 272 to store the received data and/or code. The memory 272
may receive input control signals the enable the memory 272 to
output data and/or code that was previously stored. The memory 272
may receive input control signals that enable the memory 272 to
delete data and/or code that was previously stored.
[0032] The receiver 284 may perform digital receiver functions that
may comprise, but are not limited to, fast Fourier transform
processing, beamforming processing, equalization, demapping,
demodulation control, deinterleaving, depuncture, and decoding. The
transmitter 286 may perform digital transmitter functions that
comprise, but are not limited to, coding, puncture, interleaving,
mapping, modulation control, inverse fast Fourier transform
processing, beamforming processing. The RF front end 280 may
receive analog RF signals via antennas 276a, . . . , 276n,
converting the RF signal to baseband and generating a digital
equivalent of the received analog baseband signal. The digital
representation may be a complex quantity comprising I and Q
components. The RF front end 280 may also transmit analog RF
signals via an antenna 278a, . . . , 278n, converting a digital
baseband signal to an analog RF signal.
[0033] In operation, the processor 282 may receive data from the
receiver 284. The processor 282 may perform analysis and further
processing on the received data. The processor 282 may generate a
plurality of bits that are communicated to the receiver 284. The
processor 282 may generate signals to control the operation of the
modulation process in the transmitter 286, and of the demodulation
process in the receiver 284. The processor 282 may compute weights
that may be utilized for beamforming at the transmitter 286, and/or
that may be utilized for detection at the receiver 284. The
processor 282 may store and/or retrieve information stored in the
memory 272 related to transmitter impairments, for example EVM. The
processor 282 may utilize transmitter impairment information when
computing weights for the transmitter 286 and/or receiver 284.
[0034] FIG. 3 is an exemplary diagram illustrating beamforming that
may be utilized in connection with an embodiment of the invention.
Referring to FIG. 3 there is shown a transmitting mobile terminal
302, a receiving mobile terminal 322, and a plurality of RF
channels 342. The transmitting mobile terminal 302 comprises a
transmit filter coefficient block P 304, and a plurality of spatial
streams s.sub.1 306, s.sub.2 308, and s.sub.K 310, where K may
represent a number of spatial streams transmitted by the
transmitting mobile terminal 302. The transmitting mobile terminal
may further comprise a plurality of transmitting antennas 312, 314,
and 316. The number of transmitting antennas may be represented by
the variable N. The receiving mobile terminal 322 comprises a
receive filter coefficient block Q.sup.H 324, a plurality of
destination streams s.sub.1 326, s.sub.2 328, and s.sub.K 330, and
a plurality of receiving antennas 332, 334, and 336. The number of
receiving antennas may be represented by the variable M. An
exemplary mobile terminal may be a WLAN station 104, for
example.
[0035] In operation, the transmitting antenna 312 may enable
transmission of a signal x.sub.1, the transmitting antenna 314 may
enable transmission of a signal x.sub.2, and the transmitting
antenna 316 may enable transmission of a signal x.sub.N. In a
beamforming operation, each of the transmitted signals x.sub.i,
x.sub.2, x.sub.N may be a function of a weighted summation of at
least one of the plurality of the spatial streams s.sub.i, s.sub.2,
. . . , s.sub.K. The weights may be determined by a beamforming
matrix P in connection with the transmit coefficient filter block
304.
[0036] The receiving antenna 332 may receive a signal y.sub.i, the
receiving antenna 334 may receive a signal y.sub.2, and the
receiving antenna 336 may receive a signal y.sub.M. The plurality
of RF channels 342 may be characterized mathematically by a
transfer coefficient matrix H. The transfer coefficient matrix H
may also be referred to as a channel estimate matrix.
[0037] Each of the plurality of concurrently received signals
y.sub.1, y.sub.2, . . . , y.sub.M, may be computed based on the
plurality of transmitted signals x.sub.1, x.sub.2, . . . , x.sub.N,
and the transfer coefficient matrix H, and a noise vector N. The
vector N may comprise a vector representation of noise that may
exist in the communications medium, for example.
[0038] In a system for geometric mean decomposition (GMD) with LST
detection, the matrix H may be represented by a decomposition, as
in the following equation:
H=QRP.sup.H equation[1]
where Q may represent a matrix, P may represent a beamforming
matrix utilized at a MIMO transmitter 302, P.sup.H may represent an
Hermitian transpose for the beamforming matrix P, and Q.sup.H may
represent an Hermitian transpose for the matrix Q utilized at a
MIMO receiver 322. The matrix R may represent an upper triangular
matrix, or a lower triangular matrix in various embodiments of the
invention. The matrix elements associated with the matrix R may
each be represented by a real number.
[0039] Various embodiments of the invention may utilize GMD, which
may also be referred to as uniform channel decomposition (UCD). In
various embodiments of the invention that utilize GMD, the diagonal
matrix elements in the matrix R may be equal such that
r.sub.ii=r.sub.jj, where r.sub.ii may represent an i.sup.th
diagonal matrix element and r.sub.jj may represent a value
associated with a j.sup.th diagonal matrix element.
[0040] In general, an exemplary channel estimate matrix, H may
comprise an M.times.N representation, for example
H = [ h 11 h 1 N h M 1 h MN ] or equation [ 2 ] H = [ h 1 h N ]
equation [ 3 ] ##EQU00001##
[0041] where M represents the number of rows and N represents the
number of columns, and where h.sub.i may represent a column vector
comprising matrix elements from an i.sup.th column of the matrix
H.
[0042] In general, an exemplary K.times.K upper triangular matrix R
may be represented as in the following equation:
R = [ r 11 r 12 r 1 K 0 r 22 r 23 r 2 K 0 0 r KK ] equation [ 4 ]
##EQU00002##
where each r.sub.ij may represent a matrix element within the
matrix R as shown in equation[1].
[0043] An exemplary K.times.M matrix Q.sup.H may be represented as
in the following equation:
Q H = [ w 11 w 12 w 1 M w 21 w 22 w 2 M w K 1 w K 2 w KM ] or
equation [ 5 ] Q H = [ w 1 T w 2 T w K T ] equation [ 6 ]
##EQU00003##
where each w.sub.jk, in equation[5], may represent a weight
utilized in connection with the receive filter coefficient block
Q.sup.H 324, and each w.sub.i.sup.T, in equation[6], may represent
a row vector comprising matrix elements from an i.sup.th row of the
matrix Q.sup.H. The row vector w.sub.i.sup.T may represent a
transpose for a column vector w.sub.i. The column vector w.sub.i
may comprise weights utilized in connection with computations for
an i.sup.th destination stream. An exemplary column vector w.sub.i
may comprise M.times.1 dimensions.
[0044] A minimum mean-square error (MMSE) analysis may be utilized
to compute values for corresponding weights for each of the column
vectors w.sub.i as in the following equation:
w.sub.i=R.sub.I+N.sup.-1(i)h*.sub.i equation[7]
where h.sub.i* may represent a complex conjugate for the column
vector h.sub.i, as described in equation[3], and R.sub.I+N(i) may
represent a correlation matrix of interference plus noise for an
i.sup.th destination stream. In some conventional MIMO receivers
the conjugate of the correlation matrix, R*.sub.I+N(i), may be
computed as in the following equation, for example:
R I + N * ( i ) = k = i + 1 N h k h k H + N C equation [ 8 ]
##EQU00004##
where N.sub.C may represent an M.times.M noise diagonal matrix,
where M may represent the number of receiving antennas, and
h.sub.k.sup.H may represent an Hermitian transpose for the column
vector h.sub.k. The diagonal matrix N.sub.C may comprise a
plurality of matrix elements n.sub.ii where i is an index that
refers to the i.sup.th receiving antenna. Accordingly, the matrix
element n.sub.ii may correspond to a measure of noise power
received at the i.sup.th receiving antenna.
[0045] The computation from equation[8] may not enable a
conventional MIMO receiver to compensate for transmitter-induced
noise as measured based on an error vector magnitude (EVM).
Consequently, transmitter-induced noise may be received as an
uncompensated noise signal characterized by a signal power, or
error vector power, represented by the variable .sigma..sup.2.
[0046] In various embodiments of the invention, the MIMO receiver
322 may compensate for the error vector power when utilizing
various LST/SIC and/or GMD methods, for example. In an exemplary
embodiment of the invention, the MIMO receiver 322 may compute
values associated with the complex conjugate of the correlation
matrix of interference plus noise as in the following equation:
R I + N * ( i ) = k = i + 1 N h k h k H + N C + .sigma. 2 HH H
equation [ 9 ] ##EQU00005##
where H.sup.H may represent an Hermitian transpose for the channel
estimate matrix H.
[0047] In various embodiments of the invention, a MIMO receiver 322
may compensate, or null, transmitter-induced noise while decoding
information contained in a plurality of signals received from the
MIMO transmitter 302 based on the complex conjugate correlation
matrix as computed in equation[9]. From the computation in
equation[9] column vectors w.sub.i may be computed according to
equation[7], for example. This method, when utilized in connection
with general LST/SIC approaches without the constraints associated
with GMD-based methods, may be referred to as nulling without GMD
optimization.
[0048] In a conventional GMD method, a MIMO transmitter may compute
values associated with the beamforming matrix P in the presence of
channel noise, as represented by the diagonal matrix N.sub.C, based
on an augmented channel estimate matrix H.sub.a, as in the
following equation:
H a = [ H N 0 I N ] = QRP H equation [ 10 ] ##EQU00006##
where, H may represent the channel estimate matrix as in equation
[2], N.sub.0 may represent a geometric mean noise power value
computed based values contained in the diagonal matrix N.sub.C, and
I.sub.N may represent an N.times.N identity matrix. The matrix R
may represent a K.times.K upper triangular matrix, wherein the rank
of the matrix H may be equal to K. The augmented channel estimate
matrix H.sub.a may be represented as an (M+N).times.N matrix. Based
on the augmented channel estimate matrix, the beamforming matrix P
may be computed.
[0049] A shortcoming in the computation of equation [10] is that
computation of the beamforming matrix P may not include
compensation for transmitter-induced noise. As a consequence, at
the MIMO receiver 322, an SNR measurement may not be approximately
equal for each of the transmitted spatial streams.
[0050] In various embodiments of the invention, the error vector
power value, .sigma..sup.2, may be utilized to compute a
beamforming matrix P at the MIMO transmitter 302 that includes
compensation for transmitter-induced noise. As a consequence, at
the MIMO receiver 322, an SNR measurement may be approximately
equal for each of the transmitted spatial streams. This method may
be referred to as nulling with beamforming, or GMD,
optimization.
[0051] In various embodiments of the invention, an augmented
channel estimate matrix with GMD optimization, {tilde over
(H)}.sub.a, may be computed as in the following equation:
H ~ a = [ A - 1 H N 0 I N ] = QRP H equation [ 11 ]
##EQU00007##
where the matrix A may represent an adjustment, which may be
computed based on the following equation, for example:
AA H = 1 N 0 ( .sigma. 2 HH H + N C ) equation [ 12 ]
##EQU00008##
where the matrix A.sup.H may represent an Hermitian transpose for
the matrix A.
[0052] In various embodiments of the invention, the beamforming
matrix P may be computed based on the augmented channel estimate
matrix with GMD optimization as in equation[11].
[0053] FIG. 4 is an exemplary figure illustrating packet error rate
performance, in accordance with an embodiment of the invention.
FIG. 4 presents simulation results, which illustrates packet error
rate (PER) performance as a function of SNR level for the
corresponding received signal. Parameters associated with the
results presented in FIG. 4 may be based on a GMD method for which
EVM noise power .sigma..sup.2=-30 dB, a modulation type of 64 QAM,
and a coding rate R=.sup.3/4. The MIMO transmitter 302 may comprise
3 transmitting antennas, the MIMO receiver 322 may comprise 3
receiving antennas. Referring to FIG. 4, there is shown a graph
representing PER performance without nulling 402, a graph
representing PER performance with nulling without GMD optimization
404, a graph representing nulling with GMD optimization 406, and a
graph representing no transmitter-induced noise 408.
[0054] The graph representing no transmitter-induced noise 408 may
represent an ideal condition of a MIMO transmitter 302 in the
absence of transmitter impairments, for which .sigma..sup.2=0. In
this ideal case, the lowest PER rates may be obtained for the
lowest SNR values among the graphs 402, 404, 406, and 408 as shown
in FIG. 4. The graph for nulling with GMD optimization 406
represents various embodiments of the invention in the presence of
transmitter impairments. In such embodiments, SNR measurements may
be approximately equal for each spatial stream at the MIMO
transmitter 302, and for each destination stream at the MIMO
receiver 322. In the case of nulling with GMD optimization, the
second lowest PER rates may be obtained for the second lowest SNR
values among the graphs 402, 404, 406, and 408 as shown in FIG. 4.
For example, a PER of 0.01 may be achieved for an SNR of about 27
dB in the no transmitter-induced noise case 408, while this PER
level may be achieved for an SNR of about 29 dB in the nulling with
GMD optimization case 406.
[0055] The graph representing nulling with GMD optimization 404
also represents various embodiments of the invention in the
presence of transmitter impairments. In such embodiments, SNR
measurements may not be approximately equal for each spatial stream
at the MIMO transmitter 302, and for each destination stream at the
MIMO receiver 322. In the case of nulling without GMD optimization,
the third lowest PER rates may be obtained for the third lowest SNR
values among the graphs 402, 404, 406, and 408 as shown in FIG. 4.
For example, a PER of 0.01 may be achieved for an SNR of about 27
dB in the no transmitter-induced noise case 408, while this PER
level may be achieved for an SNR of about 32 dB in the nulling
without GMD optimization case 404.
[0056] The graph representing the no nulling case 402 represents a
MIMO system in which there may be no attempt to compensate for
transmitter impairments. In the case of no nulling, the highest PER
rates may be obtained based on SNR values among the graphs 402,
404, 406, and 408 as shown in FIG. 4. For example, a PER of 0.01
may be achieved for an SNR of about 27 dB in the no
transmitter-induced noise case 408, while this PER level may be
achieved for an SNR of about 35 dB in the no nulling case 402.
[0057] FIG. 5 is a flowchart illustrating exemplary steps for
nulling at a MIMO receiver without GMD optimization, in accordance
with an embodiment of the invention. Referring to FIG. 5, in step
502, a correlation matrix of interference plus noise may be
computed by the MIMO receiver 322. In step 504, a compensated
version of the correlation matrix of interference plus noise may be
computed by the MIMO receiver 322. The compensated version of the
correlation matrix may compensate for transmitter-induced noise in
signals receiver from the MIMO transmitter 302. In step 506,
weights utilized at a MIMO receiver 322 for receiving a plurality
of signals from a MIMO transmitter 302 may be computed by the MIMO
receiver 322.
[0058] FIG. 6 is a flowchart illustrating exemplary steps for
nulling at a MIMO transmitter with GMD optimization, in accordance
with an embodiment of the invention. Referring to FIG. 6, in step
602, an augmented channel estimate matrix may be computed for the
GMD method by the MIMO transmitter 302. The augmented channel
estimate matrix may not compensate for transmitter-induced noise.
In step 604, an augmented channel estimate matrix with GMD
optimization may be computed by the MIMO transmitter 302. The
matrix computed in step 604 may comprise compensation for
transmitter-induced noise. In step 606, a MIMO transmitter 302 may
compute beamforming weights based on the transmitter-induced noise
compensation, for generating a concurrently transmitted plurality
of signals. The compensation performed at the transmitter 302 may
enable the MIMO receiver 322 to compensate for transmitter-induced
noise when detecting destination streams contained in the received
plurality of concurrent signals in accordance with GMD
constraints.
[0059] In various embodiments of the invention, aspects of a system
for minimizing effects of transmitter impairments in MIMO
beamforming communication systems may comprise a transmitter 302
that enables nulling of transmitter-induced noise by adjusting at
least a portion of a plurality of signals transmitted based on a
transmitter error vector magnitude (EVM). The transmitter 302 may
enable transmission of the plurality of signals subsequent to the
nulling.
[0060] The transmitter 302 may also enable computation of a
modified channel estimate matrix based on a channel estimate
matrix, a channel noise matrix, a channel noise value based on the
channel noise matrix, and/or the transmitter EVM. The transmitter
302 may also enable computation of beamforming weights utilized for
adjusting the signals based on the modified channel estimate
matrix. The transmitter 302 may enable generation of the plurality
of signals by combining of a plurality of spatial streams based on
the beamforming weights.
[0061] In one aspect of the invention, a signal to noise ratio
(SNR) for one of the plurality of spatial streams may be
approximately equal to a corresponding SNR for each remaining one
of the plurality of spatial streams. In addition, a modulation type
of one of the plurality of spatial streams may also be utilized for
each remaining one of the plurality of spatial streams. These
conditions may be met in MIMO communication systems that utilize a
GMD approach, for example.
[0062] In another aspect of the invention, an SNR for one of the
plurality of spatial streams may differ from a corresponding SNR
for one or more of a remainder of the plurality of spatial streams.
In addition, a modulation type of one of the plurality of spatial
streams may be a different modulation type from a corresponding
modulation type utilized for the one or more of the remaining
plurality of spatial streams. These conditions may be met in MIMO
communications systems that utilize LST and/or SVD approaches, for
example.
[0063] In various other embodiments of the invention, aspects of a
system for minimizing effects of transmitter impairments in MIMO
beamforming communication systems may comprise a receiver 322 that
enables nulling of transmitter-induced noise contained in a
plurality of received signals based on a transmitter EVM. Each of
the plurality of received signals may comprise information
contained in a plurality of spatial streams. The receiver 322 may
enable detecting estimated values for the information contained in
the plurality of spatial streams based on the nulling.
[0064] The receiver 322 may enable computation of an upper
triangular matrix, a lower triangular matrix, or a diagonal matrix
based on a channel estimate matrix, a channel noise matrix, and the
transmitter EVM. The channel estimate matrix may comprise a
plurality of channel estimates values associated with an RF
channel. The RF channel may be utilized for receiving the plurality
of received signals. The channel noise matrix may comprise values
for channel noise associated with the RF channel. The receiver 322
may also enable computation of weights utilized for detecting the
estimated values based on the upper triangular matrix, lower
triangular matrix, and/or diagonal matrix.
[0065] FIG. 7 is a block diagram for an exemplary system for
minimizing effects of transmitter impairments based on
Tomlinson-Harashima precoding (THP) using GMD, in accordance with
an embodiment of the invention. Referring to FIG. 7, there is shown
a transmitter 702, an RF channel 704, a receiver 706, and an adder
function 708. The transmitter 702 may comprise an adder block 712,
a modulus block 714, a beamforming block 716, and a feedback block
718. The RF channel 704 may be characterized by a channel estimate
matrix, H, 724. The receiver 706 may comprise a beamforming block
732, and a modulus block 734.
[0066] The transmitter 702 may be substantially as described for
the transmitter 302 (FIG. 3). The receiver 706 may be substantially
as described for the receiver 322. The RF channel 704 may be
substantially as described for the plurality of RF channels 342.
The beamforming block 716 may be substantially as described for the
transmit coefficient filter block P 304. The beamforming block 732
may be substantially as described for the receive filter
coefficient block Q.sup.H 324.
[0067] The adder function 708 may represent the addition of noise,
as represented by a noise vector n, to a plurality of signals
transmitted via the RF channel 704. Consequently, the receiver 706
may receive a plurality of signals as described in connection with
FIG. 3, y.sub.1, y.sub.2, . . . , y.sub.M, whose values may be
computed based on the plurality of transmitted signals x.sub.1,
x.sub.2, . . . , x.sub.N, the channel estimate matrix H, and the
noise vector n.
[0068] The adder block 712 may comprise suitable logic, circuitry,
and/or code that may enable binary addition and/or binary
subtraction of corresponding digital representations of input
signals to produce an output signal. The output signal may comprise
a digital representation of the addition and/or subtraction of the
corresponding input signals.
[0069] The modulus block 714 may comprise suitable logic,
circuitry, and/or code that may be utilized to generate an output
signal value that is about equal to a modulus value of an input
signal. The modulus value may be computed for a numerical base
value associated with the modulus function performed by the modulus
block 714. For example, if the numerical base value is 4, then the
output signal value may be about equal to the modulus 4 value for
the input signal. The modulus block 734 may be substantially as
described for the modulus block 734.
[0070] The feedback block 718 may comprise suitable logic,
circuitry, and/or code that may be utilized to enable generation of
an output vector representation of a plurality of output signals
based on an input vector representation of a plurality of input
signals, and a generated matrix. The generated matrix may be
represented as an upper triangular or lower triangular matrix. The
output vector may comprise a feedback vector.
[0071] In operation, the transmitter 702 may transmit a plurality
of signals x.sub.1, x.sub.2, . . . , x.sub.N, as in FIG. 3. The
transmitted signal may comprise transmitter induced noise, for
example due to cross talk noise introduced by circuitry within the
transmitter 702 that results in coupling of the transmitted
plurality of signals. The receiver 706 may receive a corresponding
plurality of signals y.sub.1, y.sub.2, . . . , y.sub.M, via the RF
channel 704 wherein the received plurality of signals comprises the
transmitter induced noise that was introduced at the transmitter
702. In various embodiments of the invention, a THP method
utilizing GMD may be employed to null transmitter induced noise at
the transmitter 702 by adjusting signal values for at least a
portion of the plurality of transmitted signals x.sub.1, x.sub.2, .
. . , x.sub.N.
[0072] The transmitter 702 may receive an input vector d. The input
vector d may represent a plurality of spatial streams, s.sub.1,
s.sub.2, s.sub.K, as in FIG. 3. The adder block 712 may receive the
input vector d, and a feedback vector .DELTA., to generate an
output vector d'. The modified vector d' may comprise values at
least a portion of which may represent adjusted signal values for
corresponding values in the input vector d.
[0073] The feedback vector .DELTA. may be computed by the feedback
block 718. The value for the feedback vector may be computed based
on the following equation, for example:
.DELTA.=(B-I)mod.sub.base(d') equation[13]
where mod.sub.base(x) may represent a modulus value for x given a
numerical base value according to the variable base, B may
represent a matrix computed by the feedback block 718, and I may
represent an identity matrix.
[0074] The modulus function mod.sub.base(d'), computed by the
modulus block 714, may enable the computed values to stay within a
determined range of values as determined by the variable base.
[0075] The feedback vector .DELTA., computed by the feedback block
718 may enable the transmitter 702 to null transmitter induced
noise, in accordance with various embodiments of the invention. The
matrix B may be computed based on the augmented channel estimate
matrix, {tilde over (H)}.sub.a, as in equation[11], from which a
matrix W is computed based on an MMSE analysis as in the following
equation:
W=R.sub.I+N.sup.-1H*.sub.a equation[14]
where M* is a complex conjugate of the matrix M, and R*.sub.I+N is
a conjugate correlation matrix of interference plus noise. Based on
equation[14], the matrix B may be computed as in the following
equation, for example:
B=W{tilde over (H)}.sub.aP.cndot. I.sub.U equation[15]
where .cndot. may represent a Hadamard product operation, P may
represent the beamforming P matrix, and I.sub.U may represent an
upper triangular matrix wherein each of the nonzero matrix elements
comprises a value of 1.
[0076] The matrix B may be an upper triangular matrix in which each
of the diagonal matrix elements comprise a value of 1.
Consequently, for the matrix (B-I), each of the diagonal matrix
elements may comprise a value of 0.
[0077] By utilizing an augmented channel estimate matrix {tilde
over (H)}.sub.a, the feedback vector .DELTA. may comprise values
based on EVM noise power at the transmitter 702. Consequently, the
feedback vector may be utilized to null transmitter induced noise
in signals transmitted by the transmitter 702 based on a THP method
utilizing GMD.
[0078] The beamforming block 716 may generate transmitted signals
based on the vector of modulus values mod.sub.base(d') computed by
the modulus block 714. The receiver 706 may perform beamforming on
the received plurality of signals based on the beamforming block
732, and utilize the modulus block 734 to compute corresponding
modulus values.
[0079] Various embodiments of the invention may not be limited to
GMD and/or LST methods. The invention may also be practiced in
systems that utilize other methods, for example singular value
decomposition (SVD).
[0080] Accordingly, the present invention may be realized in
hardware, software, or a combination of hardware and software. The
present invention may be realized in a centralized fashion in at
least one computer system, or in a distributed fashion where
different elements are spread across several interconnected
computer systems. Any kind of computer system or other apparatus
adapted for carrying out the methods described herein is suited. A
typical combination of hardware and software may be a
general-purpose computer system with a computer program that, when
being loaded and executed, controls the computer system such that
it carries out the methods described herein.
[0081] The present invention may also be embedded in a computer
program product, which comprises all the features enabling the
implementation of the methods described herein, and which when
loaded in a computer system is able to carry out these methods.
Computer program in the present context means any expression, in
any language, code or notation, of a set of instructions intended
to cause a system having an information processing capability to
perform a particular function either directly or after either or
both of the following: a) conversion to another language, code or
notation; b) reproduction in a different material form.
[0082] While the present invention has been described with
reference to certain embodiments, it will be understood by those
skilled in the art that various changes may be made and equivalents
may be substituted without departing from the scope of the present
invention. In addition, many modifications may be made to adapt a
particular situation or material to the teachings of the present
invention without departing from its scope. Therefore, it is
intended that the present invention not be limited to the
particular embodiment disclosed, but that the present invention
will include all embodiments falling within the scope of the
appended claims.
* * * * *