U.S. patent application number 11/352581 was filed with the patent office on 2006-06-15 for system for estimating data using iterative fast fourier transform error correction.
This patent application is currently assigned to InterDigital Technology Corporation. Invention is credited to Jung-Lin Pan, Ariela Zeira.
Application Number | 20060126704 11/352581 |
Document ID | / |
Family ID | 26672907 |
Filed Date | 2006-06-15 |
United States Patent
Application |
20060126704 |
Kind Code |
A1 |
Pan; Jung-Lin ; et
al. |
June 15, 2006 |
System for estimating data using iterative fast fourier transform
error correction
Abstract
A system for estimating data from a received plurality of data
signals in a code division multiple access communication system.
The data signals are transmitted in a shared spectrum at
substantially the same time. A receiver receives a combined signal
of the transmitted data signals and a sampling device samples the
combined signal. A channel estimating device estimates a channel
response for the transmitted data signals. A data detection device
estimates data of the data signals using the samples and the
estimated channel response. The data estimation uses a Fourier
transform based data estimating approach. An error in the data
estimation introduced from a circulant approximation used in the
Fourier transform based approach is iteratively reduced.
Inventors: |
Pan; Jung-Lin; (Selden,
NY) ; Zeira; Ariela; (Huntington, NY) |
Correspondence
Address: |
VOLPE AND KOENIG, P.C.;DEPT. ICC
UNITED PLAZA, SUITE 1600
30 SOUTH 17TH STREET
PHILADELPHIA
PA
19103
US
|
Assignee: |
InterDigital Technology
Corporation
Wilmington
DE
|
Family ID: |
26672907 |
Appl. No.: |
11/352581 |
Filed: |
February 13, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10004370 |
Nov 1, 2001 |
7027489 |
|
|
11352581 |
Feb 13, 2006 |
|
|
|
60282387 |
Apr 6, 2001 |
|
|
|
Current U.S.
Class: |
375/147 ;
375/E1.025; 375/E1.026 |
Current CPC
Class: |
H04B 1/7105 20130101;
H04B 2201/70707 20130101; H04B 1/71052 20130101 |
Class at
Publication: |
375/147 |
International
Class: |
H04B 1/707 20060101
H04B001/707 |
Claims
1. A system for estimating data received from a plurality of data
signals in a code division multiple access communication (CDMA)
system, the data signals transmitted in a shared spectrum at
substantially the same time, the method comprising: a receiver for
receiving a combined signal of the transmitted data signals over
the shared spectrum; a sampling device for sampling the received
signal; a channel estimating device for estimating a channel
response for the transmitted data signals; and a data detection
device for estimating data of the data signals using the samples
and the estimated channel response, using a Fourier transform based
data estimation approach, and iteratively reducing an error in the
data estimation introduced from a circulant approximation used in
the Fourier transform based approach.
2. The system of claim 1 wherein the Fourier transform based data
estimation approach is a fast Fourier transform based data
estimation approach.
3. The system of claim 1 wherein the Fourier transform based data
estimation approach uses a single user detection based data
estimation approach.
4. The system of claim 1 wherein the Fourier transform based data
estimation approach uses a multiuser detection based data
estimation approach.
5. A system for selectively reducing errors in estimating data
received from a plurality of data signals in a code division
multiple access (CDMA) communication system, the data signals
transmitted in a shared spectrum at substantially the same time,
the method comprising: a receiver for receiving a combined signal
of the transmitted data signals over the shared spectrum; a
sampling device for sampling the received signal; a channel
estimation device for estimating a channel response for the
transmitted data signals; a data detection device for estimating
data of the data signals using the samples and the estimated
channel response, using a Fourier transform based data estimating
approach, the Fourier transform based data estimating approach
using a circulant approximation; and an error correction device for
selectively reducing an error in the data estimation introduced
from the circulant approximation in the Fourier transform based
approach.
6. The system of claim 5 wherein the error correction device
iteratively reduces the error in the data estimation and the method
further comprising controlling a number of iterations performed by
the error correction device.
Description
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/004,370, filed Nov. 1, 2001, which claims
priority from U.S. Provisional Patent Application No. 60/282,387,
filed on Apr. 6, 2001, which are incorporated herein by reference
as if fully set forth.
BACKGROUND
[0002] The invention generally relates to wireless communication
systems. In particular, the invention relates to data detection in
a wireless communication system.
[0003] FIG. 1 is an illustration of a wireless communication system
10. The communication system 10 has base stations 12.sub.1 to
12.sub.5 (12) which communicate with user equipments (UEs) 14.sub.1
to 14.sub.3 (14). Each base station 12 has an associated
operational area, where it communicates with UEs 14 in its
operational area.
[0004] In some communication systems, such as code division
multiple access (CDMA) and time division duplex using code division
multiple access (TDD/CDMA), multiple communications are sent over
the same frequency spectrum. These communications are
differentiated by their channelization codes. To more efficiently
use the frequency spectrum, TDD/CDMA communication systems use
repeating frames divided into time slots for communication. A
communication sent in such a system will have one or multiple
associated codes and time slots assigned to it. The use of one code
in one time slot is referred to as a resource unit.
[0005] Since multiple communications may be sent in the same
frequency spectrum and at the same time, a receiver in such a
system must distinguish between the multiple communications. One
approach to detecting such signals is multiuser detection (MUD). In
MUD, signals associated with all the UEs 14, users, are detected
simultaneously. Another approach to detecting a multi-code
transmission from a single transmitter is single user detection
(SUD). In SUD, to recover data from the multi-code transmission at
the receiver, the received signal is passed through an equalization
stage and despread using the multi-codes. Approaches for
implementing MUD and the equalization stage of SUD include using a
Cholesky or an approximate Cholesky decomposition. These approaches
have a high complexity. The high complexity leads to increased
power consumption, which at the UE 14 results in reduced battery
life. To reduce the complexity, fast fourier transform (FFT) based
approaches have been developed for MUD and SUD. In some FFT
approaches, an approximation is made to facilitate the FFT
implementation. This approximation results in a small error being
introduced in the estimated data. Accordingly, it is desirable to
have alternate approaches to detecting received data.
SUMMARY
[0006] A system for estimating data from a received plurality of
data signals in a code division multiple access communication
system. The data signals are transmitted in a shared spectrum at
substantially the same time. A receiver receives a combined signal
of the transmitted data signals and a sampling device samples the
combined signal. A channel estimating device estimates a channel
response for the transmitted data signals. A data detection device
estimates data of the data signals using the samples and the
estimated channel response. The data estimation uses a Fourier
transform based data estimating approach. An error in the data
estimation introduced from a circulant approximation used in the
Fourier transform based approach is iteratively reduced.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a wireless communication system.
[0008] FIG. 2 is a simplified transmitter and a FFT based data
detection receiver using iterative error correction.
[0009] FIG. 3 is an illustration of a communication burst.
[0010] FIG. 4 is a flow chart of iterative error correction.
[0011] FIG. 5 is a flow chart of a receiver selectively using
iterative error correction.
[0012] FIG. 6 is a flow chart of an example of a FFT based SUD
using iterative error correction.
[0013] FIG. 7 is a flow chart of an example of a FFT based MUD
using iterative error correction.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0014] FIG. 2 illustrates a simplified transmitter 26 and receiver
28 using a FFT based data detection with iterative error correction
in a TDD/CDMA communication system, although iterative error
correction is applicable to other systems, such as frequency
division duplex (FDD) CDMA. In a typical system, a transmitter 26
is in each UE 14 and multiple transmitting circuits 26 sending
multiple communications are in each base station 12. The iterative
error correction receiver 28 may be at a base station 12, UEs 14 or
both.
[0015] The transmitter 26 sends data over a wireless radio channel
30. A data generator 32 in the transmitter 26 generates data to be
communicated to the receiver 28. A modulation/spreading/training
sequence insertion device 34 spreads the data and makes the spread
reference data time-multiplexed with a midamble training sequence
in the appropriate assigned time slot and codes for spreading the
data, producing a communication burst or bursts.
[0016] A typical communication burst 16 has a midamble 20, a guard
period 18 and two data fields 22, 24, as shown in FIG. 3. The
midamble 20 separates the two data fields 22, 24 and the guard
period 18 separates the communication bursts to allow for the
difference in arrival times of bursts transmitted from different
transmitters 26. The two data fields 22, 24 contain the
communication burst's data.
[0017] The communication burst(s) are modulated by a modulator 36
to radio frequency (RF). An antenna 38 radiates the RF signal
through the wireless radio channel 30 to an antenna 40 of the
receiver 28. The type of modulation used for the transmitted
communication can be any of those known to those skilled in the
art, such as quadrature phase shift keying (QPSK) or M-ary
quadrature amplitude modulation (QAM).
[0018] The antenna 40 of the receiver 28 receives various radio
frequency signals. The received signals are demodulated by a
demodulator 42 to produce a baseband signal. The baseband signal is
sampled by a sampling device 43, such as one or multiple analog to
digital converters, at the chip rate or a multiple of the chip rate
of the transmitted bursts. The samples are processed, such as by a
channel estimation device 44 and a FFT based data detection device
46, in the time slot and with the appropriate codes assigned to the
received bursts. The channel estimation device 44 uses the midamble
training sequence component in the baseband samples to provide
channel information, such as channel impulse responses. The channel
impulse responses can be viewed as a matrix, H. The channel
information and spreading codes used by the transmitter are used by
the data detection device 46 to estimate the transmitted data of
the received communication bursts as soft symbols. An iterative
error correction device 48 processes the estimated data to correct
errors resulting from the FFT based detection.
[0019] Although iterative error correction is explained using the
third generation partnership project (3GPP) universal terrestrial
radio access (UTRA) TDD system as the underlying communication
system, it is applicable to other systems and other FFT linear
equation based applications. That system is a direct sequence
wideband CDMA (W-CDMA) system, where the uplink and downlink
transmissions are confined to mutually exclusive time slots.
[0020] Data detection is typically modeled using a linear equation
per Equation 1. Zx=y Equation 1 For SUD, data detection is
typically modeled per Equations 2 and 3. r=Hs+n Equation 2 s=Cd
Equation 3 r is the received samples as produced by the sampling
device 43. H is the channel response matrix as produced using the
channel responses from the channel estimation device 44. s is the
spread data vector. The spread data vector, s, as per Equation 3,
is a vector multiplication of the channel codes C and the
originally transmitted data d.
[0021] A minimum mean square error (MMSE) approach to solving
Equation 1 is per Equations 4 and 5.
s=(H.sup.HH+.sigma..sup.2I).sup.-1H.sup.Hr Equation 4 d=C.sup.Hs
Equation 5 ().sup.H represents the complex conjugate transpose
function. .sigma..sup.2 is the standard deviation as determined by
the channel estimation device 44. I is the identity matrix.
[0022] Equation 4 acts as the channel equalization stage and
Equation 5 as the despreading. A cross channel correlation matrix
is defined per Equation 6. R=H.sup.HH+.sigma..sup.2I Equation 6 The
linear equation required to be solved is per Equation 7. Rs=y
Equation 7 y is per Equation 8. y=H.sup.Hr Equation 8 Although R is
not circulant for a multiple of the chip rate sampling, a portion
of R is circulant. The circulant portion is derived by eliminating
the bottom and top W rows. W is the length of the channel impulse
response. By approximating R as a circulant matrix, R.sub.cir,
R.sub.cir is decomposable through fourier transforms, such as per
Equation 9. R cir = D D - 1 .times. .LAMBDA. .times. .times. D P =
1 P .times. D P * .times. .LAMBDA. .times. .times. D P Equation
.times. .times. 9 ##EQU1##
[0023] Using a column of the R matrix to approximate a circulant
version of R, the spread data vector can be determined such as per
Equation 10. F .function. ( s _ ) = F .function. ( ( H ) 1 ) F
.function. ( r _ ) F .function. ( ( R ) 1 ) Equation .times.
.times. 10 ##EQU2##
[0024] (R).sub.1 is the first column of R and (H).sub.1 is the
first column of H, although any column can be used by permuting
that column. Preferably, a column at least W columns from the left
and right are used, since these columns have more non-zero
elements.
[0025] For MUD, data detection is typically modeled per Equation
11. r=Ad+n Equation 11 A is the symbol response matrix. The symbol
response matrix is produced by multiplying the channel codes of the
transmitted bursts with each burst's channel response.
[0026] A MMSE approach to solving Equation 11 is per Equations 12
and 13. d=(A.sup.HA+.sigma..sup.2I).sup.-1A.sup.Hr Equation 12
R=A.sup.HA+.sigma..sup.2I Equation 13 R is referred to as the cross
correlation matrix.
[0027] The linear equation to be solved is per Equation 14.
d=R.sup.-1y Equation 14 y is per Equation 15. y=A.sup.Hr Equation
15
[0028] If the elements of R are grouped into K by K blocks, the
structure of R is approximately block-circulant. K is the number of
bursts that arrive simultaneously. The K bursts are superimposed on
top of each other in one observation interval. For the 3GPP UTRA
TDD system, each data field of a time slot corresponds to one
observation interval. Using a block circulant approximation of R,
R.sub.bcir, R.sub.bcir is decomposable through block-Fourier
transforms, such as per Equation 16. R bcir = D P - 1 .times.
.LAMBDA. .times. .times. D P = 1 P .times. D P * .times. .LAMBDA.
.times. .times. D P Equation .times. .times. 16 ##EQU3## D.sub.P is
per Equation 17. D.sub.P=D.sub.N{circle around (.times.)}I.sub.K,
P=KN.sub.S Equation 17 D.sub.N is the N-point FFT matrix and
I.sub.K is the identity matrix of size K. {circle around (.times.)}
represents the kronecker product. N.sub.S is the number of data
symbols in a data field. .LAMBDA. is a block-diagonal matrix. The
blocks of .LAMBDA. are D.sub.PR.sub.cir(:,1:K). As a result,
.LAMBDA. is per Equation 18. .LAMBDA.=diag(D.sub.PR(:,1:K))
Equation 18 Using FFTs, the data vector, d, is determined per
Equation 19. F(d)=.LAMBDA..sup.-1F(A.sup.Hr) Equation 19
[0029] As a result, the FFT of d is determined. The data is
estimated by taking the inverse FFT of d.
[0030] To improve on the accuracy, the dimension of R may be
extended to include the impulse response of the last symbol. This
last symbol's impulse response extends into either the midamble or
guard period. To capture the last symbol's response, the
block-circulant structure of R is extended another W-1 chips. W is
the length of the impulse response. The same FFT approach is
performed by using the extended R matrix and extended r by using
information from the midamble (after midamble cancellation) or the
guard period.
[0031] If a prime factor algorithm (PFA) FFT is used, the R matrix
may be extended so that the most efficient PFA of length z is used.
The R matrix is similarly expanded to length z using the midamble
or guard period information.
[0032] Although iterative error correction is explained in
conjunction with specific implementations of SUD and MUD, it is
applicable to any FFT based solution to a linear equation, which
uses a circulant approximation, as generally described as
follows.
[0033] Equation 1 is a general linear equation. Zx=y Equation 1 To
determine x, Equation 20 can be used. x=Z.sup.-1y Equation 20
Inverting matrix Z is complex. By approximating Z as a circulant
matrix, Z.sub.cir, Z is determinable by FFT decomposition per
Equations 21 or 22. Z cir = D P - 1 .times. .LAMBDA. .times.
.times. D P = 1 P .times. D P * .times. .LAMBDA. .times. .times. D
P Equation .times. .times. 21 ##EQU4## Z cir - 1 = D P - 1 .times.
.LAMBDA. .times. .times. D P = 1 P .times. D P * .times. .LAMBDA. -
1 .times. D P Equation .times. .times. 22 ##EQU5## If Z is a
block-circulant matrix, a block-fourier transform is used instead,
which uses equations analogous to Equations 21 and 22.
[0034] The circulant approximation of Z creates an approximation
error. The difference between Z and Z.sub.cir is per Equation 23.
Z=Z.sub.cir-.DELTA..sub.Z Equation 23
[0035] .DELTA..sub.Z is the differential matrix between Z and
Z.sub.cir. Using the circulant and differential matrix, Equation 1
becomes Equation 24. (Z.sub.cir-.DELTA..sub.Z)x=y Equation 24 By
rearranging Equation 24, Equation 25 results.
x=Z.sub.cir.sup.-1y+Z.sub.cir.sup.-1.DELTA..sub.zx Equation 25 An
iterative approach can be used to solve Equation 25, per Equation
26, 27 and 28. x.sup.(k)=x.sup.((k-1))+.DELTA..sub.x.sup.((k-1))
Equation 26 x.sup.((k-1))=Z.sub.cir.sup.-1y Equation 27
.DELTA..sub.x.sup.((k-1))=Z.sub.cir.sup.-1.DELTA..sub.Zx.sup.((k-1))
Equation 28 Using Equations 26, 27 and 28, x is solved as follows.
An initial solution of x, x.sup.(0), is determined using FFTs of
Equation 21 or 22 and the circulant approximation, as illustrated
by Equation 29. x.sup.(0)=Z.sub.cir.sup.-1y Equation 29 An initial
error correction term, .DELTA..sub.x.sup.(0), is determined using
Equation 30.
.DELTA..sub.x.sup.(0)=Z.sub.cir.sup.-1.DELTA..sub.Zx.sup.(0)
Equation 30 The initial error correction term,
.DELTA..sub.x.sup.(0), is added to the initial solution, x.sup.(0),
per Equation 31. x.sup.(1)=x.sup.(0)+.DELTA..sub.x.sup.(0) Equation
31 Iterations are repeated N times per Equations 32 and 33.
.DELTA..sub.x.sup.(k)=Z.sub.cir.sup.-1.DELTA..sub.Zx.sup.(k)
Equation 32 x.sup.(k+1)=x.sup.(k)+.DELTA..sub.x.sup.(k), k=1, 2, .
. . N-1 Equation 33 x.sup.(N) is used as the estimate for x. The
number of iterations, N, may be a fixed number based on a trade-off
between added complexity due to the iterative error correction
process and increased accuracy in the x determination. The number
of iterations, N, may not be fixed. The iterations may continue
until x.sup.(k+1) and x.sup.(k) are the same value (the solution
converges) or their difference is below a threshold, such as per
Equations 34 and 35, respectively. x.sup.(k+1)-x.sup.(k)=0 Equation
34 x.sup.(k+1)-x.sup.(k)<T Equation 35 T is the threshold
value.
[0036] One of the advantages to iterative error correction is that
it is optional. The initial solution, x.sup.(0), is the same
estimation of x as would result without any error correction. As a
result, if additional precision in the x determination is not
necessary, iterative error correction is not performed and the
additional complexity of iterative error correction is avoided.
However, if additional precision is necessary or desired, iterative
error correction is performed at the expense of added complexity.
Accordingly, iterative error correction provides an adaptable
tradeoff between accuracy and complexity. Furthermore, since
iterative error correction is performed only using matrix
multiplications, additions and subtractions, the added complexity
of iterative correction is relatively small.
[0037] Applying iterative error correction to a MMSE solution for
SUD, the linear equation being solved is per Equation 36. Rs=y
Equation 36 y and R are per Equations 37 and 13, respectively.
y=H.sup.Hr Equation 37 R=H.sup.HH+.sigma..sup.2I Equation 13 In
relating Equation 36 to Equation 1, R corresponds to Z, s
corresponds to x and y corresponds to y.
[0038] Using a circulant approximation for R, R.sub.cir, iterative
error correction is performed as follows. An initial spread data
estimate, s.sup.(0), is determined using FFTs, as illustrated by
Equation 38. s.sup.(0)=R.sub.cir.sup.-1y Equation 38 An initial
error correction term, .DELTA..sub.s.sup.(0), is determined using
Equation 39.
.DELTA..sub.s.sup.(0)=R.sub.cir.sup.-1.DELTA..sub.Rs.sup.(0)
Equation 39
[0039] .DELTA..sub.R is the difference between R and R.sub.cir. The
initial error correction term, .DELTA..sub.s.sup.(0), is added to
the initial solution, s.sup.(0), per Equation 40.
s.sup.(1)=s.sup.(0)+.DELTA..sub.s.sup.(0) Equation 40
[0040] Iterations are repeated N times per Equations 41 and 42.
.DELTA..sub.s.sup.(k)=R.sub.cir.sup.-1.DELTA..sub.Rs.sup.(k)
Equation 41 s.sup.(k+1)=s.sup.(k)+.DELTA..sub.s.sup.(k), k=1, 2, .
. . N-1 Equation 42 The data symbols are determined using the
N.sup.th iterations estimated spread data vector, s.sup.(N), by
despreading using the channel codes of the transmitted bursts as
illustrated in Equation 43. d=C.sup.Hs.sup.(N) Equation 43
[0041] Applying iterative error correction to a MMSE solution for
MUD, the linear equation being solved is per Equation 44. Rd=y
Equation 44 y and R are per Equations 45 and 46, respectively.
y=A.sup.Hr Equation 45 R=A.sup.HA+.sigma..sup.2I Equation 46 In
relating Equation 44 to Equation 1, R corresponds to Z, d
corresponds to x and y corresponds to y.
[0042] Using the block circulant approximation for R, R.sub.bcir,
iterative error correction is performed as follows. An initial data
estimate, d.sup.(0), is determined using FFTs, as illustrated by
Equation 47. d.sup.(0)=R.sub.bcir.sup.-1y Equation 47
[0043] An initial error correction term, .DELTA..sub.R.sup.(0), is
determined using Equation 48.
.DELTA..sub.R.sup.(0)=R.sub.bcir.sup.-1.DELTA..sub.Rd.sup.(0)
Equation 48
[0044] .DELTA..sub.R is the difference between R and R.sub.bcip.
The initial error correction term, .DELTA..sub.d.sup.(0), is added
to the initial solution, d.sup.(0), per Equation 49.
d.sup.(1)=d.sup.(0)+.DELTA..sub.d.sup.(0) Equation 49
[0045] Iterations are repeated N times per Equations 50 and 51.
.DELTA..sub.d.sup.(k)=R.sub.bcir.sup.-1.DELTA..sub.Rd.sup.(k)
Equation 50 d.sup.(k+1)=d.sup.(k)+.DELTA..sub.d.sup.(k), k=1, 2, .
. . N Equation 51
[0046] The estimated data symbols is the Nth iterations estimated
data symbols, d.sup.(N). An analogous approach is also used if the
R matrix is extended to capture the last symbols impulse response
or extended to an efficient PFA length.
* * * * *