The present invention relates to a technique for reducing nonlinear distortion in amplifiers, and more particularly, to digital predistortion based on a power series model.
It is necessary for wireless transmission to sufficiently compensate for nonlinear distortion generated in power amplifiers when appropriately transmitting amplitude-varying signals using a linear modulation scheme. Digital predistortion is a technique for canceling distortion produced in a power amplifier by adding an inverse distortion component to the signal input to the power amplifier. In order to achieve a satisfactory compensation effect, the amplitude and the phase of the distortion component to be added to the input signal have to be controlled at high accuracy.
One method for realizing the predistortion is using a lookup-table type predistorter configured to look for an appropriate distortion component from the lookup table corresponding to the input signal. This method is described in H. Girard and K. Feher, “A New Baseband Linearizer for More Efficient Utilization of Earth Station Amplifiers Used for QPSK Transmission”, IEEE J. Select Areas Commun., Vol.SAC-1, No. 1, 1983.
From the viewpoint of achieving more accurate distortion compensation, a power series predistorter that represents the nonlinear distortion characteristic of the power amplifier using a power series model is known. See, for example, Okamoto, Nojima, and Ohoyama, “Analysis and Compensation of nonlinear distortion in a travelling-wave tube amplifier based on IF Band Predistortion”, IEICE Technical Study Report, MW76-112, 1976.
U.S. Pat. No. 5,164,678 issued to Puri et al, entitled “Process for Compensating nonlinearities in an Amplifier Circuit” discloses automatic control for a power-series predistorter. In this publication, the output signal from the power amplifier and the respective degrees of distortion components generated by a digital predistorter are subjected to fast Fourier Transform (FFT) to perform frequency conversion, and the coefficients of the respective degrees are estimated.
Similarly, G. Lazzarin, S. Pupolin, and A. Sarti, “Nonlinearity Compensation in Digital Radio Systems”, IEEE Trans. Commun., Vol.42, No. 2/3/4, February/March/April, 1994 discloses a technique for controlling polynomial coefficients of a digital predistorter. In this publication, a covariance matrix is calculated for the signal generated by the digital predistorter, and the difference between the output signal of the power amplifier and the signal generated by the digital predistorter is used as an error to control the polynomial coefficients of the predistorter.
Another publication, T. Nojima and T. Konno, “Cuber Predistortion Linearizer for Relay Equipment in 800 MHz Band Land Mobile Telephone System”, IEEE Trans. Vech. Tech., Vol.VT-34, No. 4, Nov.1985, discloses automatic control of a power-series predistorter. In this publication, the predistorter is controlled using pilot signals in certain carrier frequencies so as to allow the polynomial to follow change in temperature or change over time in the power amplifier. This technique is practically applied to transmission amplifiers of boosters for automobile telephones.
Conventional power-series type predistorters can achieve satisfactory nonlinearity (or distortion) compensation if a sufficient amount of output backoff is guaranteed, as illustrated in
The two plots 202 and 204 shown in the chart of
In general, these two distortion components are close to each other at a low power level (for example, at or below 20 dBm), as illustrated in
Meanwhile, the input signal being input to the predistorter has a certain degree of randomness, and accordingly, the memory effect may vary in response to the input signal varying over time. In other words, the frequency-dependent nonlinearity may vary over time. However, the conventional predistorters cannot follow such a change over time satisfactorily, and consideration of highly precise nonlinearity compensation has not been made sufficiently.
It may be proposed to cause the predistorter to follow the change over time in the distortion component using a pilot signal. In this case, the distortion component has to be compensated for using a pilot signal within, for example, the period of training sequence, independently from the signal transmission. However, because the pilot signal cannot always be acquired, it is difficult to easily and accurately compensate for the distortion using a pilot signal. In addition, compensating for the distortion using a pilot signal includes many steps, such as inputting a prescribed pilot signal to the predistorter, supplying the output of the predistorter to the power amplifier, scanning the entire frequency range to detect nonlinear distortion components, and controlling various parameters so as to reduce the detected distortion components. Accordingly, the process and the structure may become complicated.
Since it is proposed to use broadband modulation signals in the near future for wireless communication systems, highly precise compensation for distortion components is required for broad band signals over several tens of megahertz (MHz). As the frequency range to be used increases, the change in the frequency-dependent nonlinear distortion components is likely to increase, and therefore, the problem will become more serious.
Therefore, it is an object of the present invention to solve the above-described problems in the prior art, and to provide a digital predistorter capable of highly precise nonlinear distortion compensation for a power amplifier based on power series analysis.
To achieve the object, in one aspect of the invention, a digital predistorter using a power series model to compensate for nonlinear distortion of a power amplifier is provided. The digital predistorter comprises:
With this arrangement, the tap coefficient of the finite impulse response filter is adaptively controlled so as to introduce the nonlinear distortion component that can efficiently cancel the distortion generated in the power amplifier, and accordingly, distortion compensation accuracy can be improved.
The reference signal is, for example, a feed-forward signal derived from the digital input signal. With this arrangement, the adaptive control is performed based on the signal that has not been subjected to amplification, and therefore, the control speed can be increased.
The reference signal may be a feedback signal derived from the output of the power amplifier. In this case, the adaptive control is performed based on the actually amplified signal, and therefore, distortion compensation accuracy can be further improved.
The feedback signal is obtained by, for example, subtracting a first signal in proportion to the digital input signal or to a power of the digital input signal from a second signal derived from an output of the power amplifier. By removing the dominant linear component (i.e., the fundamental wave), a nonlinear distortion component that is to be compensated for can be extracted.
The adaptive controller may be configured to receive both the feed-forward signal and the feedback signal as the reference signals. In this case, the adaptive controller adjusts the tap coefficient of the finite impulse filter so as to reduce the difference between the feed-forward signal and the feedback signal.
In another aspect of the invention, a transmitter using a digital predistorter is provided. The transmitter comprises a power amplifier configured to amplify a digital transmission signal, and a digital predistorter connected to the power amplifier and configured to compensate for nonlinear distortion of the power amplifier using a power series model. The digital predistorter includes:
With this arrangement, the transmitter can transmit a signal under efficient control of nonlinearity compensation.
Other objects, features, and advantages of the invention will become more apparent from the following detailed description when read in conjunction with the accompanying drawings, in which
The preferred embodiments of the present invention are now described in detail in conjunction with the attached drawings. The same components are denoted by the same reference numbers throughout the drawings. In the first embodiment, feedback control is employed. In the second embodiment, feed-forward control is employed. In the third embodiment, both feedback control and feed-forward control are employed.
The signal transmission system of the transmitter includes a digital predistorter 302, a digital-to-analog converter (DAC) 304, an orthogonal modulator 306, a frequency converter 308, and a power amplifier 310. The feedback system of the transmitter includes a directional coupler 312, a frequency converter 314, an orthogonal demodulator 316, and an analog-to-digital converter (ADC) 318. The digital predistorter 302 has a pair of coefficient multipliers 320, a pair of adders 322, a pair of distortion generating units 324, and an adaptive controller 326.
The digital predistorter 302 receives a digital signal to be transmitted (referred to as a “digital transmission signal”), as indicated at the top left of the figure. The inphase component (I component) and the quadrature component (Q component) of the digital signal are input to the digital predistorter 302 separately from each other. The digital transmission signal is generally a baseband signal, as in the embodiment; however, it may be a signal of an intermediate frequency band, depending on the use. The inphase component and the quadrature component of the digital transmission signal are supplied to the associated coefficient multipliers 320, and the amplitude and/or the phase of each component is adjusted by an amount corresponding to an appropriate fixed number “a1” (generally, a complex number). Each of the adjusted components is supplied to one of the input terminals of the associated adder 322.
The inphase component and the quadrature component of the input digital signal are also supplied to the associated distortion generating units 324, which generate nonlinear distortion signals for the corresponding components. The nonlinear distortion signal of each component is supplied to the other input terminal of the associated adder 322. The adaptive controller 326 controls the operation of the distortion generating units 324. The detailed operation of the digital predistorter 302 is described below.
Each of the digital-to-analog converters 304 converts one of the nonlinear distortion-added digital inphase component and quadrature component output from the digital predistorter 302 into an analog form.
The orthogonal modulator 306 combines the inphase component (I) and the quadrature component (Q) into a modulation signal. The modulation signal y(t) is represented as
y(t)=yi(m)cos(2πft)−yq(m)sin(2πft)
where yi(m) and yq(m) denote the inphase component and the quadrature component, respectively, of the m-th symbol of the digital transmission signal.
The frequency converter 308 upconverts the baseband or intermediate-band modulation signal to a radio frequency (RF) signal.
The power amplifier 310 amplifies the power level of the RF signal so as to be suitable for radio transmission. The output signal of the power amplifier 310 contains a distortion component generated by nonlinear amplification, as well as a linearly amplified signal component. The influence of the nonlinear distortion is cancelled by inverse distortion given by the digital predistorter 302 to the digital transmission signal prior to the power amplification The signal output from the power amplifier 310 is treated as an output of the transmitter, and transmitted from an antenna (not shown).
On the other hand, the directional coupler 312 of the feedback system extracts a portion of the amplified signal to be transmitted. The frequency converter 314 downconverts the radio frequency band of the extracted signal to a baseband or an intermediate band. The orthogonal demodulator 316 separates the downconverted signal into an inphase component (I) and a quadrature component (Q). The analog-to-digital converters 318 convert the analog inphase component and the analog quadrature component to digital forms, respectively, and supply the digitally-converted components to the adaptive controller 326.
Each of the coefficient multipliers 320, 404, and 414 multiplies the input signal by a prescribed constant (generally, a complex number) indicated as “a1”, “a3”, or “a5” in the figure. The third order multiplier 402 raises the input signal to the third power, and the fifth order multiplier 412 raises the input signal to the fifth power. Each of the FIR3 406 and the FIR5 416 estimates and outputs a weighted average of the input signal and the past data (previously input signals). The weighting is called a tap coefficient. In general, the digital filter (FIR filter) produces an output signal by multiplying the output of each of the delay elements connected in series by a weighting factor and combining the weighted outputs. Alternatively, the digital filter may be configured so as to use Fourier transforms and inverse Fourier transforms to perform the major arithmetic operations at the frequency range. Such digital signal processing can be executed using existing means, such as microprocessors, DSPs, or FPGAs.
The basic operation of the digital predistorter 302 of the first embodiment is now explained. A digital transmission signal input to the digital predistorter 302 is denoted as u(m), where m denotes a parameter designating the number of samplings. If the sampling interval is T, the sampling time t is expressed as
t=mT. (1)
The output x1 of the coefficient multiplier 320 is expressed as
x1=a1*u(m). (2)
The output x3 of the third order FIR filter (FIR3) 406 is expressed as
x3=a3*(w3BH)*U3(m), (3)
where W3B is the (N+1)-dimensional vector consisting of (N+1) tap coefficients of the third order FIR filter (FIR3), w3BH is the complex conjugate transpose of vector W3B, and U3 (m) is the (N+1)-dimensional vector consisting of the current and past signals input to the third order FIR filter (FIR3). The complex conjugate transpose of weighting vector W3B and the complex conjugate transpose of the input signal vector U3 (m) are expressed as
W3BH=(w0(m), w1(m), . . . , wN (m)) (4)
U3(m)H=(u3(m), u3(m−1), . . . , u3(m−N)) (5)
Similarly, the output x5 of the fifth order FIR filter (FIR5) 416 is expressed as
x5=as*(w5BH)*U5(m),
where w5B is the (N+1)-dimensional vector consisting of (N+1) tap coefficients of the fifth order FIR filter (FIR5), and U5(m) is the (N+1)-dimensional vector consisting of the current and past signals input to the fifth order FIR filter (FIR5). The higher order signal components x7, x9, . . . can be obtained in the same manner.
The output x1 of the coefficient multiplier 320 corresponds to a linearly amplified digital transmission signal. The output x3 of the third order FIR filter (FIR3) corresponds to the third order distortion (nonlinear) component represented by the nonlinearly amplified signal, and the output x5 of the fifth order FIR filter (FIR5) corresponds to the fifth order distortion (nonlinear) component represented by the nonlinearly amplified signal. In the same manner, the seventh and the higher order distortion components can be obtained. The distortion components represented by the nonlinearly amplified signals X3, X5, . . . are added to the digital transmission signal at the digital predistorter 302. Accordingly, the output y(m) of the digital predistorter 302 becomes
As is known in the art, the nonlinear distortion components are expressed as odd-order terms. Although the output signal y(m) described above only represents one of the inphase component and the quadrature component, the actual output signal of the digital predistorter 302 contains the inphase component yi(m) and the quadrature component yq(m). The output of the predistorter 302 (containing the inphase and quadrature components) is then converted to a modulation signal y(t), which is expressed as
y(t)=yi(m)cos(2πft)−yq(m)sin(2πft). (7)
If the modulated signal y(t) is input to the power amplifier 310, the output z(t) of the power amplifier 310 is expressed as
which is a power series of the input signal y(t). The i-th order distortion component is expressed as the i-th order term of the power series polynomial (8). The coefficient bi of the term represents the contribution of the i-th distortion component.
Although there are DACs 304, orthogonal modulator 306, and frequency converter 308 existing between the digital predistorter 302 and the power amplifier 310, the signal processing carried out by these components is not essential to the present invention, and explanation of them is omitted here. In this embodiment, both the output of the digital predistorter 302 and the input to the power amplifier 310 are treated as y(t) for the purpose of simplification.
Returning to
Zmon(m)=b1y(m)+b3y3(m)+b5y5(m)+ . . . (11)
Furthermore, using Equation (6) for expressing y(m), the feedback signal Zmon (m) is further expressed as
Next, focusing is made on the output signal x1 of the coefficient multiplier 320 (in which the distortion components have not been introduced yet by the distortion generating unit 324). Assuming that only signal x1 is input to the power amplifier 310, the output signal z1 of the power amplifier contains a linear component due to linear amplification of signal x1 and a nonlinear component due to nonlinear amplification of signal x1. Accordingly, output signal z1 is expressed as
z1(m)=c1x1(m)+c3x1(m)3+c5x1(m)5+ . . . (13)
where ci denotes the coefficient of the term of i-th power. The power series coefficient ci can be determined from the input and output characteristics of the power amplifier 310.
The difference between the feedback signal Zmon (m) and the hypothetical output signal z1(m) is obtained by subtracting Equation (13) from Equation (12).
If the estimated power series coefficient ci of the power amplifier and the actual power series coefficient bi are equal to each other (bi=ci), and if the higher order terms are omitted, then Equation (14) is rewritten as
Depending on product use, the seventh and higher order terms x7(m), x9(m), . . . may be omitted. The difference Zmon(m)−z1(m) represents an output signal obtained when only the distortion components x3(m), x5(m), . . . generated by the distortion generating unit 324 of the digital predistorter 302 are linearly amplified at the power amplifier 310. Each of the terms in Equation (15) corresponds to the error signal ezi+1 of the associated order (e.g., the third order, the fifth order, . . . ). The tap coefficient of each of the FIR filters is adaptively adjusted so as to minimize the terms of Equation (15). Since the tap coefficients are adaptively controlled in accordance with the frequency-dependency or the change over time of the distortion component, efficient predistortion can be realized.
Next, focusing is made on the third order distortion component x3 produced at the distortion generating unit 324. The error signal e3(m) for the third order distortion is obtained by subtracting the estimated contribution of the seventh and higher order terms from the difference Zmon(m)−z1(m).
If the estimated power series coefficient ci of the power amplifier equals the actual power series coefficient bi (bi=ci), the error signal e3 (m) is expressed simply as
e3(m)=b1*x3(m). (17)
By adaptively controlling the tap coefficient of the third order FIR filter (FIR3) so as to minimize the error signal e3(m), a distortion component x3 that can cancel the third order distortion introduced by the power amplifier 310 can be generated at the distortion generating unit 324.
Similarly, the error signal e5(m) for the fifth order distortion component is expressed simply as
e5(m)=b1*x5(m). (18)
By adaptively controlling the tap coefficient of the fifth order FIR filter (FIR5) so as to minimize the error signal e5(m), a distortion component x5 that can cancel the fifth order distortion introduced by the power amplifier 310 can be generated at the distortion generating unit 324. The distortion components x2i+1 that can cancel the higher order distortion components are also generated in the same manner.
The error signal e2i+1 is an evaluation function that has to be made as small as possible in adaptive control. It can be understood from Equations (17) and (18) that the error signals do not contain thermal noise or random errors. Accordingly, the error signals can be minimized, independent of thermal noise or random errors, in the adaptive control of the tap coefficients. To perform adaptive control itself, many existing algorithms, such as a steepest descent method, an LMS method, or an RLS, can be used. Alternatively, a Kalman filter may be used.
Zmon(m)=b1(x1+x3)+b3(x1+x3)3+ . . . (19)
The output of the coefficient multiplier 502 is c1x1, which corresponds to z1 explained in the previous example. Accordingly, the output of the subtractor 504 represents an error signal, which is represented as
Zmon(m)−z1(m)=(b1−c1)x1+b1x1=b1x3=e3
where b1=c1. The adaptive algorithm unit 506 receives the error signal e3, and adjusts the tap coefficient of the filter FIR3 406 so as to minimize the error signal e3 by executing an adaptive algorithm described above.
Zmon(m)=b1(x1+x3)+b3(x1+x3)3+ . . . (20)
The output of the coefficient multiplier 502 is c1x1, and the output of the other coefficient multiplier 602 is c1x13. The sum of these two outputs corresponds to z1 explained above. Accordingly, the output of the subtractor 606 represents an error signal, which is represented as
Zmon(m)−z1(m)=(b1−c1)x1+(b3−c3)x13+b1x3=b1x3=e3
where b1=c1 and b3=c3. The adaptive algorithm unit 507 receives the error signal e3, and adjusts the tap coefficient of the filter FIR3 406 so as to minimize the error signal e3. In this example, the term (b3−c3)x13 of Equation (14) is considered, unlike the example shown in
Zmon(m)=b1(x1+x3+x5)+b3(x1+x3+x5)3+b5(x1+x3+x5)5 . . . (21)
The output of the subtractor 706 becomes
Zmon(m)−(c1x1+c3x13+c5x15)=(b1−c1)x1+(b3−c3)x13+(b5−c5)x15+b1x3+b1x5. (22)
Equation (22) corresponds to Equation (14). Since the output of the coefficient multiplier 708 is c1x5, the output of the subtractor 710 becomes
(b1−c1)x1+(b3−c3)x13+(b5−x5)x15+b1x3+(b1−c1)x5=b1x3=e3,
where b1=c1, b3=c3, and b5=c5. This output is an error signal for the third order distortion. The adaptive algorithm unit 716 receives the error signal e3, and adaptively controls the tap coefficient of filter FIR3 406 so as to minimize the error signal e3.
In step 1110, a back filter (or a postfilter) FIRB placed on the output side of the i-th order multiplier is selected as the current filter whose tap coefficient is to be controlled. For example, the back filter FIRB 407 for the third order distortion is selected. In step 1112, adaptive control is performed to determine the tap coefficient so as to minimize the error signal (or the reference signal) ei. In step 1114, it is determined whether the tap coefficients of all the back filters FIRB positioned at the output ends of the multipliers have been controlled. If there is an uncontrolled back filter FIRB still existing (NO in S1114), the degree “i” is incremented, and the process returns to step 1110 to repeat steps 1110, 1112, and 1114. If all the back filters have been controlled in step 1114, then the process terminates at step 1116.
In this embodiment, the tap coefficient of the front filter placed before the multiplier is adjusted, and then, the tap coefficient of the back filter placed after the multiplier is adjusted. However, the adjusting order may be changed. Simultaneous adjustment at both the input end and the output end is unsuitable because the number of parameters being varied increases, and time and workload required to converge to the appropriate solution may increase.
Each of the coefficient multipliers 1204, 1210, and 1220 multiplies the input signal by a prescribed constant (generally, a complex number) indicated as “a1”, “a3”, or “a5” in the figure. The third order multiplier 1208 raises the input signal to the third power, and the fifth order multiplier 1218 raises the input signal to the fifth power. Each of FIR3 and FIR5 estimates and outputs a weighted average of the input signal and the past data (previously input signals).
The basic operation of the digital predistorter 1202 of the second embodiment is now explained. A digital transmission signal input to the digital predistorter 1202 is denoted as u(m), where m denotes a parameter designating the number of samplings. If the sampling interval is T, the sampling time t is expressed as
t=mT. (23)
The output x1 of the coefficient multiplier 1204 is expressed as
x1=a1*u(m). (24)
The output x3 of the third order FIR filter (FIR3) 406 is expressed as
x3=a3*(w3BH)*U3(m), (25)
where w3B is the (N+1)-dimensional vector consisting of (N+1) tap coefficients of the third order FIR filter (FIR3), w3BH is the complex conjugate transpose of vector w3B, and U3(m) is the (N+1)-dimensional vector consisting of the current and past signals input to the third order FIR filter (FIR3). The complex conjugate transpose of weighting vector w3B a and the complex conjugate transpose of the input signal vector U3(m) are expressed as
w3BH=(w0(m), w1(m), . . . , wN(m)) (26)
U3(m)H=(u3(m), u3(m−1), . . . , u3(m−N)). (27)
Similarly, the output x5 of the fifth order FIR filter (FIR5) is expressed as
x5=a5*(w5BH)*U5(m)
where w5B is the (N+1)-dimensional vector consisting of (N+1) tap coefficients of the fifth order FIR filter (FIR5), and U5(m) is the (N+1)-dimensional vector consisting of the current and past signals input to the fifth order FIR filter (FIR5). The higher order signal components X7, x9, . . . can be obtained in the same manner.
The adaptive controller 1226 of the second embodiment receives the digital transmission signal u(m) input to the digital predistorter 12012, and creates a new weighting factor based on the received signal u(m) and the past weighting information. For example, the weighting vector w3B for the third order distortion is determined based on a recurrence equation, such as
w3B(m)=w3B(m−1)+F3B (u(m)), (28)
where F3B is the (N+1)-dimensional updating vector that depends on the digital transmission signal u(m). The updating vector is selected depending on the employed adaptive algorithm. For example, a covariance matrix R is determined by estimating the matrix elements from the digital transmission signal u(m) using the Wiener-Hopf method, and the next weighting vector W3B (m) can be obtained by multiplying the present and past transmission signal U(m)H=(u(m), u(m−1), . . . , u(m−N)) by the determined covariance matrix R. The initial value of the weighting vector may be set in advance in the adaptive algorithm by measuring the frequency-dependency of the distortion component of the power amplifier 310 in advance. Alternatively, the initial value may be set to zero at the beginning, using an algorithm that can learn to find the appropriate initial value through the running of the algorithm.
Since the digital predistorter 1202 of the second embodiment does not require a feedback loop, the tap coefficient can be controlled promptly. The signal processing required for the feedback loop is eliminated, and the adaptive control can be carried out using a simple structure, although, from the viewpoint of improving precision, the feedback control of the first embodiment is preferable.
The adaptive controller 1326 receives a digital transmission signal u(m), which is a feed-forward signal supplied through the feed-forward path, as well as a feedback signal u′ (m) through the feedback path, which is generated from the signal having actually passed through the power amplifier 310. The adaptive control is performed to adjust the tap coefficients so as to minimize the difference e(m) between the feed-forward signal u(m) and the feedback signal u′ (m).
e(m)=u(m)−u′(m) (29)
The error signal e(m) does not contain thermal noise or random errors, and therefore, highly precise adaptive control can be performed. The tap coefficient (or the weighing factor) of the third order FIR filter can be determined based on the following recurrence equation
w3B(m)=w3B(m−1)+F3B(e(m)), (30)
where F3B is the (N+1) -dimensional updating vector that depends on the digital error signal e(m), and it differs depending on the employed adaptive algorithm. The weighting factors for the fifth and higher order distortions can be determined in the same manner.
The subtractor 1404 outputs an error signal e(m), which represents the difference between the feed-forward signal u(m) and the appropriately level-adjusted feedback signal u′ (m), to the adaptive algorithm unit 1406. The adaptive algorithm unit 1406 controls the tap coefficient of the filter FIR3 1212 so as to minimize the error signal e(m), using a known adaptive algorithm, such as one described above.
The subtractor 1720 generates and outputs an error signal e3(m) with respect to the third order distortion, to the adaptive algorithm unit 1716. The adaptive algorithm unit 1716 controls the tap coefficient of the filter FIR3 1212 so as to minimize the error signal e3(m) The subtractor 1722 generates and outputs an error signal e5(m) with respect to the fifth order distortion, to the adaptive algorithm unit 1718. The adaptive algorithm unit 1718 controls the tap coefficient of the filter FIR5 1222 so as to minimize the error signal e5(m)
In the examples shown in
This patent application is based on and claims the benefit of the earlier filing dates of Japanese Patent Application No. 2004-021031 filed Jan. 29, 2004, the entire contents of which are hereby incorporated by reference.
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2004-021031 | Jan 2004 | JP | national |
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