CN101267187A - A self-adapted pre-distortion method and system for broadband linear power amplifier - Google Patents
A self-adapted pre-distortion method and system for broadband linear power amplifier Download PDFInfo
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Abstract
The present invention discloses a self-adapting predistortion method of a broadband linearization power amplifier and a system thereof, wherein the convergence character of the self-adapting predistortion algorithm itself is used. The self-adapting algorithm adopted by the predistortion unit is executed with dynamic selection according to the value of the error signal. The signal outputted by the broad band power amplifier passes through a down frequency-converting and high-speed ADC and is then transmitted to the self-adapting algorithm unit for comparing with the desired signal thereby obtaining the error signal of the two sides. The self-adapting algorithm of the corresponding convergence character is dynamically selected according to the magnitude of the error signal. As the self-adapting predistortion method of the invention is combined with the character of rapid convergence algorithm and smooth convergence algorithm, the velocity of predistortion estimating is increased and the operand is reduced. The complexity degree for realizing the hardware of the algorithm is reduced.
Description
Technical Field
The invention belongs to the technical field of wireless communication, relates to a predistortion technology of a linearized power amplifier adopted by a transmitter of a broadband wireless communication system, and particularly relates to a self-adaptive predistortion method and a self-adaptive predistortion system suitable for the broadband linearized power amplifier.
Background
The amplifier is the main device of communication and radar system, and due to the development of linear modulation technology and the adoption of non-constant envelope modulation technology, the amplifier puts higher requirements on the linearity of the power amplifier.
In the development process of wireless communication, improving transmission quality and spectrum utilization rate is always a source for promoting the development of wireless communication technology, and a modulation mode with higher efficiency generally has higher requirement on the linearity of a transmitter at a transmitting end, so that the power amplifier linearization technology becomes a key technology of a next generation wireless communication system. In modern wireless communication systems, a power amplifier is an important device, and when signals are transmitted, the signals must be linearly amplified and frequency-converted (up-converted and down-converted) at the transmitting end and the receiving end of the communication system. During the amplification of a signal by a power amplifier, information is lost due to amplitude distortion and phase distortion of the signal (assuming that both the amplitude and the phase of the signal carry information), resulting in an increase in Bit Error Rate (BER). Therefore, the linearity performance of the power amplifier is a very important indicator in the design of modern communication systems. As is known, the available bandwidth of the radio frequency spectrum is limited, and on the other hand, the channel capacity is also limited, as can be seen from the channel capacity formula of Shannon (Shannon). Therefore, as the number of communication users increases, the channel becomes more and more congested, and in order to ensure a low error rate in transmission, a power amplifier is required to have good linearity performance. In practical applications, power amplifiers are usually operated in a nonlinear region close to the saturation region in order to improve efficiency. This non-linear characteristic introduces harmonic components that not only degrade the signal-to-noise ratio, but also often cause cross-talk between channels, thereby causing distortion and distortion of the signal.
In addition, in the conventional mobile communication system, constant envelope Modulation is generally emphasized, and FM (Frequency-Modulation Frequency Modulation) and GMSK (Gaussian Minimum Shift Keying) are examples. In these modulation techniques, the power amplifier operates in a nonlinear region near the saturation region to achieve high power efficiency without generating intermodulation products that affect adjacent channels. Each channel adopts a power amplifier, and the power amplifiers are filtered by a heavy narrow-band filter and then synthesized to be output. However, constant envelope modulation can only utilize a limited frequency spectrum, placing pressure on the communication system. With the development of mobile communication, the requirement for the capacity of a communication system is higher and higher, and meanwhile, in order to fully utilize frequency resources and improve the transmission rate of the system, linear Modulation techniques such as QPSK, 16QAM (16 Quadrature Amplitude Modulation) and multicarrier Modulation techniques such as OFDM, WCDMA and the like are more and more widely applied. Signal envelopes using these modulation techniques vary due to fluctuating envelopes of the linear or multi-carrier modulated signals, and the varying signal envelopes are sensitive to power amplifier non-linearities, which can increase non-linear distortion of the amplifier signal. These fluctuations will generate intermodulation components after passing through the nonlinear rf power amplifier, and will generate adjacent channel interference, which seriously affects the communication quality, so it is required to use the power amplifier with high linearity to suppress the output intermodulation interference. These all put higher demands on the linearity of the power amplifier.
Therefore, the linearization technique of the power amplifier is becoming an important research focus in the wireless communication field. A number of linearization techniques have been proposed in the past decade, the main of which are: back-off, feed-forward and predistortion.
Predistortion techniques are one of the best ways to compensate for amplifier nonlinear distortion. Using this technique, the nonlinear distortion of the power amplifier is cancelled with an inverse distortion at the input of the power amplifier. If the anti-distortion characteristics are designed to vary with changes in the operating point (output power) of the amplifier, then adjusting the distortion can compensate for the degradation in system performance caused by operating point changes due to temperature, supply voltage, tube aging, etc. Currently, the predistortion technology includes three methods, i.e., radio frequency predistortion, intermediate frequency predistortion, and baseband predistortion. The radio frequency predistortion technology has the advantages of easy realization, low cost and the like, and has the defects that a radio frequency nonlinear active device is used, is difficult to adjust and control, and cannot realize faster self-adaption. The intermediate frequency predistorter can compensate the nonlinear distortion caused by the third intermodulation of the power amplifier by adjusting the coefficient of the predistorter. However, this method uses an analog circuit to achieve linearization, and has a limited degree of improvement in nonlinear distortion. The baseband predistortion technology processes signals at a baseband and can be realized by a Digital Signal Processor (DSP) and an adaptive predistortion algorithm. The baseband predistortion technology does not relate to complex radio frequency signal processing, only processes baseband signals, is easy to adapt, and is convenient to realize by adopting the modern digital signal processing technology, so the baseband predistortion technology is a better linearization method.
The baseband predistortion technology is a technology for completing predistortion processing in the digital field, and generally has two implementation modes, namely a parameter model implementation based on a nonlinear radio frequency power amplifier and a mode implementation based on a lookup table. There are many parametric models for rf power amplifiers, such as polynomial models, Volterra series models. The polynomial predistortion system is the popularization of a third-order predistortion system, and after an analog polynomial predistorter appears, a polynomial predistortion system realized by a digital technology appears and is gradually developed and perfected. The Volterra series is a general model for describing a nonlinear system, can solve the memory effect of the nonlinearity of an amplifier, and is one of the hot spots of the current research.
In the present mobile communication field, overcoming multipath interference and improving communication quality is a very important problem, especially when the channel characteristics are not fixed, the problem is particularly prominent, and the occurrence of the adaptive filter perfectly solves the problem. The core of the adaptive predistortion filter is an adaptive algorithm, and a block diagram of a system for implementing adaptive predistortion processing by using the adaptive algorithm in the prior art is shown in fig. 1. There are two typical adaptive algorithms: least Mean Square (LMS) and Recursive Least Squares (RLS). The basic goal of an Adaptive predistortion Filter (Adaptive Filter) is to adjust its parameters in such a way that the output of the Filter minimizes as much as possible the objective function containing a particular reference signal. The method of adjusting the filter parameters is an Adaptive Algorithm (Adaptive Algorithm), which has a range to be used for each based on different aspects.
The LMS algorithm is the most widely used adaptive optimization algorithm. It is proposed based on minimum mean square error criterion (MMSE) wiener filter and steepest descent method. According to the idea of the steepest descent algorithm, the update value of the weight vector at n +1 can be calculated by the following simple recursive relationship:
then there are:
W(n+1)＝W(n)+μ(rxd-RxxW) (2)
in practice, R needs to be known if formula (2) is used directlyxxAnd rxdIs difficult to know, the obvious approach is to replace the instantaneous estimate with:
thus, a weight vector update formula is obtained:
W(n+1)＝W(n)+μX(n)e*(n) (5)
where μ is a constant value used to control the convergence properties of the random weight vector W (n), and 0 < μ < Trace (R)xx) Wherein the Trace operation represents the Trace of the matrix, i.e. the sum of the main diagonal elements of the matrix. The minimum mean square algorithm has the main advantages of stable convergence, simple structure and convenient realization; the updating is performed sample by sample. When the statistical characteristics of the channel environment are stable and unknown, the algorithm for updating samples one by one can work well. The disadvantage is that the convergence characteristic depends on the characteristic structure of the autocorrelation matrix of the input signal, and when the spread range of the characteristic value is large, the convergence speed of the algorithm is slow.
The RLS algorithm uses an iterative method to perform the inversion of the matrix. The recurrence formula is as follows:
W(n)＝W(n-1)+g(n)[d(n)-XH(n)W(n-1)]* (6)
C(n)＝λ-1[C(n-1)-g(n)XH(n)C(n-1)]
wherein W (0) is 0 and C (0) is δ-1I and delta are relatively small positive real numbers, lambda is a forgetting factor, and the value of lambda meets the condition that lambda is more than 0 and less than 1.
The RLS algorithm has the advantage of high convergence speed, and when the signal-to-noise ratio is high, the convergence speed of the RLS algorithm is one order of magnitude faster than that of the LMS algorithm. The disadvantage is that the convergence cannot be stabilized. Secondly, it is insensitive to eigenvalue spread of the covariance matrix of the array signal and is computationally expensive.
Disclosure of Invention
It is an object of the present invention to provide a system for adaptive predistortion processing for wideband linearized power amplifiers.
Another object of the present invention is to provide an adaptive predistortion method for a broadband linearized power amplifier.
The invention provides a system for realizing the self-adaptive predistortion processing of a broadband linear power amplifier, which comprises a predistortion unit, a high-speed digital-to-analog converter (DAC), an up-conversion unit, a broadband power amplifier (WPA), a down-conversion unit, a high-speed analog-to-digital converter (ADC), an error signal judgment unit, a fast convergence self-adaptive algorithm unit and a stable convergence self-adaptive algorithm unit,
the pre-distortion unit is used for pre-distortion filtering of an input baseband signal, the pre-distortion filtered baseband signal is input to the WPA after passing through the DAC and the up-conversion unit, and part of signals in an output signal of the WPA are fed back to the error signal judgment unit after passing through the down-conversion unit and the ADC;
the error signal decision unit is used for calculating a priori error signal according to the feedback signal and the expected signal, selecting an adaptive algorithm used by the predistortion unit according to the magnitude of the priori error signal, and when the priori error signal is large, the error signal decision unit is selectively connected with the fast convergence adaptive algorithm unit, and the predistortion unit uses the fast convergence adaptive algorithm; when the prior error signal is smaller, the error signal judgment unit is connected with the stable convergence self-adaptive algorithm unit selectively, and the predistortion unit uses the stable convergence self-adaptive algorithm;
the expected signal is obtained by delaying an input baseband signal of the predistortion unit, or the expected signal is the baseband signal output by the predistortion unit.
Optionally, the system further includes a delay unit, configured to delay an input baseband signal of the predistortion unit to obtain the desired signal.
Optionally, the predistortion unit employs a polynomial kernel model.
Optionally, the predistortion unit is implemented by taking an FPGA as hardware thereof, and selects a DSP chip as an extension of a hardware platform according to the computational complexity of the adaptive algorithm, so as to improve the performance and flexibility of the predistortion algorithm.
Optionally, the sampling rate of the DAC unit reaches 1000MPS or more, and a filter is interpolated to meet the requirement of wideband baseband signal transmission.
Optionally, the fast convergence adaptive algorithm is an RLS algorithm, or an LRLS algorithm, or an FTRLS algorithm; wherein, the adaptive algorithm LMS algorithm of smooth convergence, or DLM algorithm, or DCT-LMS algorithm.
The invention also provides a self-adaptive predistortion method of the broadband linear power amplifier, wherein an input baseband signal is processed by a predistortion unit and then is input to the broadband power amplifier WPA after passing through a high-speed digital-to-analog converter DAC and an up-conversion unit, part of signals in an output signal of the WPA are fed back to an error signal decision unit after passing through a down-conversion unit and an ADC, the error signal decision unit calculates a priori error signal, and selects a proper self-adaptive algorithm to perform predistortion processing according to the value of the priori error signal, and the specific steps are as follows:
the method comprises the following steps: the error signal judgment unit calculates a priori error signal according to the feedback signal and an expected signal, wherein the expected signal is obtained by delaying an input baseband signal of the predistortion unit, or the expected signal is the baseband signal output by the predistortion unit;
step two: the error signal judgment unit selects a fast convergence self-adaptive algorithm or a stable convergence self-adaptive algorithm according to the magnitude of the prior error signal, and if the prior error signal is larger, the fast convergence self-adaptive algorithm is selected; if the prior error signal is smaller, selecting a stable convergence self-adaptive algorithm;
step three: and the predistortion unit carries out predistortion filtering according to the tap coefficient fed back by the fast convergence adaptive algorithm or the stable convergence adaptive algorithm.
Optionally, wherein the fast convergence adaptive algorithm is an RLS algorithm, or an LRLS algorithm, or an FTRLS algorithm.
Optionally, the smooth convergence adaptive algorithm is an LMS algorithm, or a DLM algorithm, or a DCT-LMS algorithm.
Optionally, the function of calculating the a priori error signal in step one is: e [ n ] -Cn [ B [ n ], where e [ n ] is the value of the prior error signal, z [ n ] is the baseband signal output by the predistortion unit, Cn is the filter coefficient of the predistortion unit, and Bn is the feedback signal
The invention can improve the performance of the self-adaptive predistortion algorithm and greatly reduce the complexity of the realization of the self-adaptive predistortion algorithm. The predistortion system in the flexible and effective implementation method of the adaptive predistortion system comprises two types of adaptive predistortion algorithms, and the adaptive predistortion algorithms can be dynamically selected according to different input signals, so that the convergence rate of a predistortion unit is increased, and the hardware implementation of the predistortion algorithms is facilitated.
The invention has the following advantages:
(1) the self-adaptive predistortion method of the broadband linear power amplifier combines the characteristics of a rapid convergence algorithm and a stable convergence algorithm, improves the speed of predistortion estimation, greatly reduces the operation amount of the predistortion estimation and reduces the expense of a predistortion system.
(2) The self-adaptive predistortion method of the broadband linearization power amplifier adopts a dynamic selection mechanism of a self-adaptive algorithm, thereby greatly reducing the complexity of realizing algorithm hardware.
Drawings
FIG. 1 is a block diagram of a prior art adaptive predistortion processing system;
fig. 2 is a block diagram of the adaptive predistortion processing system of the present invention.
Detailed Description
The following describes the embodiment of the present invention with reference to fig. 2.
The system shown in fig. 2 includes a Wideband baseband signal unit, a predistortion unit, a high-speed DAC (Digital-to-Analog Converter), an up-conversion unit, a WPA (Wideband Power Amplifier), a down-conversion unit, a high-speed ADC (Analog-to-Digital Converter), an error signal decision unit, a fast convergence adaptive algorithm unit, a smooth convergence adaptive algorithm unit, and optionally a delay unit. The predistortion unit can be implemented by using an FPGA (Field Programmable Gate array) as hardware, and a DSP (Digital Signal Processing) chip is selected as an extension of a hardware platform according to the computational complexity of the adaptive algorithm, so as to improve the performance and flexibility of the predistortion algorithm. The sampling rate of the high-speed DAC unit is required to reach more than 1000MPS, an interpolation filter is required, and the requirement of broadband baseband signal transmission is met. The key point of the implementation of the invention is the coordination estimation of the error signal decision unit and the two adaptive algorithm units.
By adopting the adaptive predistortion method proposed by the invention, the specific method implemented by the baseband predistortion system shown in fig. 2 is as follows:
(1) the wideband wireless baseband signal is an OFDM (orthogonal frequency Division Multiplexing) baseband signal modulated by QAM (Quadrature Amplitude Modulation) or QPSK (Quadrature Phase Shift Keying) or other wideband wireless signals, and the baseband signal is subjected to peak value cancellation to be used as an input signal of a predistortion unit, and then subjected to high-speed DA conversion, up-conversion and WPA to be an RF signal.
(2) Part of the RF signals are subjected to down-conversion and high-speed AD conversion to obtain feedback signals B (n), and the feedback signals B (n) enter an error signal judgment unit. The feedback signal B (n) reflects the non-linear memory distortion characteristics of WPA.
(3) Constructing a kernel of a digital predistortion algorithm, and adopting the following polynomial model as follows:
where C represents the filter coefficients, x (n) is the input to the predistortion kernel, Z (n) represents the output of the predistortion kernel, K is the order of the polynomial model, and Q is the predistortion memory effect length (i.e., the memory length of the power amplifier);
(4) the error signal decision unit calculates an a priori error signal from the desired signal and the feedback signal B (n), i.e. the feedback signal is compared with the desired signal, thereby obtaining error signals of the two. The desired signal is defined in two ways: firstly, an input signal of a predistortion unit is obtained by delaying through a delay unit and is marked as d (n), and the definition is suitable for an adaptive predistortion model of indirect inversion; secondly, the baseband signal output by the predistortion unit is marked as z [ n ], and the definition is suitable for the adaptive predistortion model of direct inversion
When a direct-inversion adaptive predistortion model is adopted, the error signal judgment unit calculates a priori error signal through the following formula:
e[n]＝z[n]-C[n]B[n] (8)
where e [ n ] is the value of the prior error signal, z [ n ] is the baseband signal output by the predistortion unit, Cn is the filter coefficient of the predistortion unit, and Bn is the feedback signal.
(5) And selecting a proper adaptive algorithm according to the magnitude of the error signal. The general rule is as follows:
a) the predistortion filter coefficient is from the initial state to the first best state, in the process, the value of an error signal is generally larger, and the convergence rate of the process is required to be faster in practical application, so that the power amplifier is rapidly brought into a stable linear working state, and the RLS and the related rapid self-adaptive algorithm are adopted. The fast convergence adaptive algorithm has larger operation amount, but can realize the fast convergence characteristic, so that the adaptive estimation approaches to the inverse of the WPA tap in a short time, and the predistorter can quickly reach the optimal working state. A fast converging adaptive algorithm should have excellent performance even in cases where the eigenvalue spread of the input signal correlation matrix is large.
b) In the adaptive process after the predistortion filter reaches the optimal state, the value of an error signal is generally small, the requirement on the convergence rate of the algorithm is low, more consideration is given to reduce the complexity of hardware implementation, and an LMS and a related simple and stable adaptive algorithm are adopted. The convergence rate of the stable convergence adaptive algorithm is lower than that of the quick convergence adaptive algorithm, but for a tiny prior error signal, the method can realize lower calculation complexity and higher convergence stability, is favorable for realizing a hardware platform, and simultaneously, because tiny related calculation is simpler, the convergence rate of the method also meets the actual requirement.
(6) The RLS algorithm based on the training sequence performs the estimation of the adaptive digital pre-distortion filter coefficients. In the formula (7), the filter coefficient C is linear, and K and Q respectively take a constant value; to increase the convergence speed, the filter coefficient C is calculated quickly.
The estimation process of the filter coefficient is to perform fast estimation of the filter coefficient C based on the RLS algorithm of the training sequence to obtain the initial value of the pre-distortion kernel filter coefficient. The RLS algorithm based on the training sequence is as follows:
a) initializing X ═ C ═ 0, 0]TAnd
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b) sending a broadband training sequence to detect the nonlinear characteristic of the power amplifier, wherein the sampling fed back to the digital predistortion synthesis unit by the radio frequency receiver is y (n), and the y (n) is the output of the broadband power amplifier;
c) accepting new input sample and feedback sample pairs { x [ n ]]，f[n]At the same time, input signal x [ n ]]And a feedback sampling signal f [ n ]]Move to reference signal vector X [ n ]]And F [ n ]]Here, the
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e) Calculating a kalman gain factor by the following formula;
f) updating the inverse autocorrelation matrix of the filter according to the following formula, p being a forgetting factor:
g) the predistortion filter coefficients are updated according to the following formula:
C[n]＝C[n-1]+k[n]e[n] (12)
h) repeating steps b through g to perform an estimation of the predistortion filter coefficients.
(7) The filter coefficient C is updated based on the LMS algorithm. Once the adaptive filter coefficient reaches the optimal value, the nonlinear distortion of the broadband power amplifier changes with the change of time due to the influence of factors such as temperature and device aging, and in order to solve the problem, the updating process of the filter coefficient C is based on an adaptive algorithm (LMS algorithm) suitable for hardware implementation to track the time-varying characteristic of the nonlinear power amplifier. The LMS algorithm is as follows:
a) initializing C by using the filter coefficient of the formula (12);
b) receiving a new pair of input samples and feedback loop samples { X [ n ], fn }, while shifting the input signal X [ n ] and the feedback sample signal fn to reference signal vectors X [ n ] and fn;
d) updating the filter coefficients according to the following formula, u being the convergence factor;
C[n+1]＝C[n]+ue[n]X[n] (13)
e) repeating steps b through g to perform an estimation of the predistortion filter coefficients.
The best application example of the present invention is described in detail above with reference to fig. 2, but the present invention is not limited to this example, for example, the definition of the error signal has flexibility, and different parameters can be selected as the judgment basis for algorithm selection; the Fast convergence adaptive algorithm is not limited to the RLS algorithm, and may be LRLS (Lattice Recursive Least squares), FTRLS (Fast reciprocal Recursive Least squares), and the like; similarly, the adaptive algorithm for simple smooth convergence is not limited to LMS, and may be DLMS (delayed LMS algorithm), DCT-LMS (Discrete Cosine Transform LMS algorithm), or the like.
The above description is only for the best mode of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A system for realizing the adaptive predistortion processing of a broadband linearized power amplifier comprises a predistortion unit, a high-speed digital-to-analog converter (DAC), an up-conversion unit, a broadband power amplifier (WPA), a down-conversion unit, a high-speed analog-to-digital converter (ADC), an error signal judgment unit, a fast convergence adaptive algorithm unit and a stable convergence adaptive algorithm unit,
the pre-distortion unit is used for pre-distortion filtering of an input baseband signal, the pre-distortion filtered baseband signal is input to the WPA after passing through the DAC and the up-conversion unit, and part of signals in an output signal of the WPA are fed back to the error signal judgment unit after passing through the down-conversion unit and the ADC;
the error signal decision unit is used for calculating a priori error signal according to the feedback signal and the expected signal, selecting an adaptive algorithm used by the predistortion unit according to the magnitude of the priori error signal, and when the priori error signal is large, the error signal decision unit is selectively connected with the fast convergence adaptive algorithm unit, and the predistortion unit uses the fast convergence adaptive algorithm; when the prior error signal is smaller, the error signal judgment unit is connected with the stable convergence self-adaptive algorithm unit selectively, and the predistortion unit uses the stable convergence self-adaptive algorithm;
the expected signal is obtained by delaying an input baseband signal of the predistortion unit, or the expected signal is the baseband signal output by the predistortion unit.
2. The system of claim 1, further comprising a delay unit for delaying an input baseband signal of the predistortion unit to obtain the desired signal.
3. The system of claim 1, wherein the predistortion unit employs a polynomial kernel model.
4. The system of claim 1, wherein the predistortion unit is implemented by using an FPGA as its hardware, and a DSP chip is selected as an extension of a hardware platform according to the computational complexity of the adaptive algorithm, so as to improve the performance and flexibility of the predistortion algorithm.
5. The system of claim 1, wherein the DAC unit has a sampling rate of 1000MPS or more and interpolates filters to meet wideband baseband signaling requirements.
6. The system of claim 1, wherein the fast convergence adaptive algorithm is an RLS algorithm, or an LRLS algorithm, or an FTRLS algorithm; wherein, the adaptive algorithm LMS algorithm of smooth convergence, or DLM algorithm, or DCT-LMS algorithm.
7. A self-adaptive predistortion method for a broadband linear power amplifier is disclosed, wherein an input baseband signal is processed by a predistortion unit and then is input to a broadband power amplifier WPA after passing through a high-speed digital-to-analog converter DAC and an up-conversion unit, part of signals in an output signal of the WPA are fed back to an error signal decision unit after passing through a down-conversion unit and an ADC, the error signal decision unit calculates a priori error signal, and selects a proper self-adaptive algorithm to perform predistortion processing according to the value of the priori error signal, and the specific steps are as follows:
the method comprises the following steps: the error signal judgment unit calculates a priori error signal according to the feedback signal and an expected signal, wherein the expected signal is obtained by delaying an input baseband signal of the predistortion unit, or the expected signal is the baseband signal output by the predistortion unit;
step two: the error signal judgment unit selects a fast convergence self-adaptive algorithm or a stable convergence self-adaptive algorithm according to the magnitude of the prior error signal, and if the prior error signal is larger, the fast convergence self-adaptive algorithm is selected; if the prior error signal is smaller, selecting a stable convergence self-adaptive algorithm;
step three: and the predistortion unit carries out predistortion filtering according to the tap coefficient fed back by the fast convergence adaptive algorithm or the stable convergence adaptive algorithm.
8. The adaptive predistortion method of claim 7, wherein the fast convergence adaptive algorithm is an RLS algorithm, or an LRLS algorithm, or an FTRLS algorithm.
9. An adaptive predistortion method as set out in claim 7, wherein the stationary convergence adaptive algorithm is the LMS algorithm, or the DLM algorithm, or the DCT-LMS algorithm.
10. The adaptive predistortion method of claim 7 wherein the function of calculating the a priori error signal in step one is: e [ n ] -z [ n ] -cn [ n ], where e [ n ] is the value of the a priori error signal, z [ n ] is the baseband signal output by the predistortion unit, cn is the filter coefficient of the predistortion unit, and bn is the feedback signal.
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