CN103546405B - A kind of method and system of pre-distortion - Google Patents
A kind of method and system of pre-distortion Download PDFInfo
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- CN103546405B CN103546405B CN201210243287.1A CN201210243287A CN103546405B CN 103546405 B CN103546405 B CN 103546405B CN 201210243287 A CN201210243287 A CN 201210243287A CN 103546405 B CN103546405 B CN 103546405B
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Abstract
The present invention provides a kind of method and system of pre-distortion, and this method includes：The data exported according to an adjustment factor to predistorter carry out pre-distortion；The adjustment factor according to the result of calculation of adaptive algorithm and one forget parameter adjustment；The forgetting parameter is adjusted according to a preset zoom factor.The time of system stabilization after renewal coefficient can be shortened according to the present invention, coordinate original adaptive algorithm can be with larger raising systematic function.
Description
Technical field
The present invention relates to moving communicating field, particularly applies one kind in the communication system of the big bandwidth such as LTE is pre- to lose
The method and system really handled.
Background technology
In wireless domain, the nonlinear characteristic of power amplifier (PA), with the increase of input signal bandwidth, and more
Substantially.This application to the big bandwidth standard such as LTE (Long Term Evolution), generates great challenge.In current solution,
Major part is to increase digital processing capabilities using digital pre-distortion (DPD) technology (structure such as Fig. 1).The technology is improving power
The linearity of amplifier (PA) simultaneously, has larger flexibility, and the advantage of relatively low cost.
In Fig. 1, PA is a nonlinear device.If data directly input from PA, then generation will be presented in PA outputs
Non-linear distortion.And predistorter is for correcting the non-linear of PA (power amplifier), allowing from input data and output data
A linear characteristic is presented, shields PA nonlinear characteristic.In addition, the data before the PA inputs that system can also be extracted and after input
Characteristic, and respective handling is done, training form is saved as, the key input coefficient as predistorter.
In the prior art, the method handled predistortion as shown in Fig. 2 be by the output of predistorter and a regulation
Number carries out computing by multiply-accumulator.The output of multiply-accumulator is exported by PA, the feedback of caching PA outputs, and PA output is anti-
Feedback and the output of multiply-accumulator carry out auto-correlation computation, the adjustment factor updated.But the following shortcoming of current structure design：
The system stabilization time updated after adjustment factor is slow；Systematic function is not high, it is impossible to fully meets the requirement of present high performance system.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of method and system of pre-distortion, to shorten renewal coefficient
The system stable time afterwards.
In order to solve the above-mentioned technical problem, the invention provides a kind of method of pre-distortion, including：
The data exported according to an adjustment factor to predistorter carry out pre-distortion；
The adjustment factor according to the result of calculation of adaptive algorithm and one forget parameter adjustment；
The forgetting parameter is adjusted according to a preset zoom factor.
Further, the above method also has following feature：It is described to be forgotten according to the result of calculation of adaptive algorithm and one
Adjustment factor includes described in parameter adjustment：
It is in advance the forgetting initial value of parameter configuration one, is stored in the first register；
The forgetting parameter that first register exports is multiplied with the zoom factor, the scaling is then subtracted with 1
The difference addition of coefficient, obtains new forgetting parameter.
Further, the above method also has following feature：It is described obtain new forgetting parameter after also include：
First register is updated using the new forgetting parameter.
Further, the above method also has following feature：It is described that the forgetting is adjusted according to a preset zoom factor
Parameter includes：
By the result of calculation of the adjustment factor of the second register cache and adaptive algorithm carry out matrix multiple, then with institute
Forgetting parameter is stated to be added to obtain the first coefficient matrix；
First coefficient matrix is inverted to obtain the second coefficient matrix；
After the adjustment factor of the second register storage is multiplied by second coefficient matrix, multiplied by with the adaptive calculation
The result of calculation of method, obtain the 3rd coefficient matrix；
After the adjustment factor of the second register storage subtracts the 3rd coefficient matrix, divided by the forgetting parameter obtains
To the 4th coefficient matrix；
Result of calculation and the adjustment factor after last time renewal of 4th coefficient matrix, the adaptive algorithm are carried out
It is cumulative, obtain the adjustment factor of this renewal.
Further, the above method also has following feature：It is described to obtain also including after the 4th coefficient matrix：
Second register is updated according to the 4th coefficient matrix.
In order to solve the above problems, present invention also offers a kind of system of pre-distortion, including：
Processing module, the data for being exported according to an adjustment factor to predistorter carry out pre-distortion；
Coefficient adjustment module, forget for the result of calculation according to adaptive algorithm and one and system is adjusted described in parameter adjustment
Number；
Parameter module is forgotten, for adjusting the forgetting parameter according to a preset zoom factor.
Further, said system also has following feature：The forgetting parameter module includes：
First register, for caching forgetting parameter, caching is the initial value of the forgetting parameter configuration in advance, by described in
Forget parameter to export to adjustment unit and coefficient adjustment module；
Adjustment unit, the forgetting parameter for first register to be exported are multiplied with the zoom factor, then with 1
The difference addition of the zoom factor is subtracted, new forgetting parameter is obtained, the new forgetting parameter is exported and posted to described first
Storage.
Further, said system also has following feature：
First register, it is additionally operable to be updated using the new forgetting parameter of adjustment unit output.
Further, said system also has following feature：The coefficient adjustment module includes：
Second register, for the adjustment factor of caching, adjustment factor is exported to first module, third unit and the 4th
Unit；
Institute's first module, for the result of calculation of the adjustment factor and adaptive algorithm to be carried out into matrix multiple, then
It is added to obtain the first coefficient matrix with the forgetting parameter；
Second unit, for being inverted to obtain the second coefficient matrix to first coefficient matrix；
The third unit, after the adjustment factor is multiplied by into second coefficient matrix, multiplied by with described adaptive
The result of calculation of algorithm is answered, obtains the 3rd coefficient matrix；
Unit the 4th, after the adjustment factor is subtracted into the 3rd coefficient matrix, divided by the forgetting parameter obtains
To the 4th coefficient matrix；
Unit the 5th, after the 4th coefficient matrix, the result of calculation of the adaptive algorithm and last time are updated
Adjustment factor added up, obtain this renewal adjustment factor.
Further, said system also has following feature：
Unit the 4th, it is additionally operable to export the 4th coefficient matrix to second register；
Second register, it is additionally operable to be updated according to the 4th coefficient matrix.
To sum up, the present invention provides a kind of method and system of pre-distortion, and system is stable after can shortening renewal coefficient
Time, coordinate original adaptive algorithm can be with larger raising systematic function.
Brief description of the drawings
Fig. 1 is common DPD theory diagrams；
Fig. 2 is the DPD structural representations of prior art；
Fig. 3 is the schematic diagram of the DPD system of the embodiment of the present invention；
Fig. 4 is the schematic diagram of the forgetting parameter module of the embodiment of the present invention；
Fig. 5 is the schematic diagram of the coefficient adjustment module of the embodiment of the present invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with accompanying drawing to the present invention
Embodiment be described in detail.It should be noted that in the case where not conflicting, in the embodiment and embodiment in the application
Feature can mutually be combined.
The result for the adaptive algorithm that Fig. 2 is is directly output to the coefficient module after renewal, causes original algorithm prominent
Under the disturbed condition of hair, when calculating optimal coefficient, convergence rate is slow.In 2G, 3G scene, this method can meet
It is required that but to that under the big bandwidth situations of 4G LTE, cannot meet to require.It is mainly reflected in, when the external world strong jamming occurs suddenly
In the case of, it is necessary to after many individual time slots, DPD performances can just return to normal level.The present invention is on original algorithm, increase
Increased coefficient adjustment module and forget parameter module, coordinate original adaptive algorithm module, reduce and calculate optimal coefficient
The convergence time of process.Common DPD structures, stepped parameter can be set, such a constant value, it is stable to carry out control system.If set
The excessive then system of value put is not stablized, if value is too small, the system stable time is slow.
In the embodiment of the present invention, add one and forget parameter module and coefficient adjustment module (with reference to figure 3), to replace step
Enter the function of parameter.It is a variable in itself to forget parameter, coordinates adaptive algorithm, can effectively be avoided because the value set does not conform to
It is suitable and on influence caused by system.
The method that pre-distortion is carried out by the system of the embodiment of the present invention comprises the following steps：
Step 11, the data exported according to an adjustment factor to predistorter carry out pre-distortion；Wherein, according to adaptive
The result of calculation and one for answering algorithm forget adjustment factor described in parameter adjustment；The forgetting is adjusted according to a preset zoom factor
Parameter.
In adaptive algorithm, stepping directly affects the performance of convergent time and last optimal coefficient.If stepping
Greatly, although convergence time is short, the coefficient finally calculated may not be optimal, and performance is not high.And stepping is small, although last coefficient energy
It is optimal, but the convergent time is grown.The embodiment of the present invention is first to use the reference value of software merit rating, as initial value, Ran Houli
Each step size is calculated with hardware itself, is started with big stepping, reduction convergence time, afterwards using small stepping, raising systematicness
Energy.
Fig. 4 is the schematic diagram of the forgetting parameter module of the embodiment of the present invention, as shown in figure 4, the forgetting parameter of the present embodiment
Module includes：
Forget parameter register (equivalent to the first register), forget parameter for caching, caching is the forgetting in advance
The initial value of parameter configuration, the forgetting parameter is exported to adjustment unit and coefficient adjustment module；
Adjustment unit, the forgetting parameter for first register to be exported are multiplied with the zoom factor, then with 1
The difference addition of the zoom factor is subtracted, new forgetting parameter is obtained, the new forgetting parameter is exported and posted to described first
Storage.
The forgetting parameter register, it is additionally operable to be updated using the new forgetting parameter of adjustment unit output.
Forget parameter module calculation process may comprise steps of：
Step 101, before coefficient is recalculated every time, first configure the initial value for forgeing parameter.
For example, the initial value for being pre-configured with forgetting parameter is 0.95.
Step 102, the renewal for the first time of forgetting parameter register are to read in initial value.
After step 103, the forgetting parameter of forgetting parameter register output are multiplied with zoom factor, scaling system is subtracted plus 1
Several differences, obtain new forgetting parameter.
An empirical value (generally 0.99 or so), the initial value as zoom factor can be provided according to PA characteristic.
Step 104, parameter register is forgotten using the forgetting parameter renewal newly obtained.
Step 103 to 104 is repeated, final forgetting parameter can be infinitely close to 1.
According to above-mentioned narration, step 102 to 104 forms a simple closed-loop system.If the scaling system of software merit rating
Number and initial value be not right, then by closed-loop control, hardware can be automatically adjusted to optimal value.
Fig. 5 is the schematic diagram of the coefficient adjustment module of the embodiment of the present invention, as shown in figure 5, the coefficient adjustment of the present embodiment
Module can include：
Second register (equivalent to adjustment factor register), for the adjustment factor of caching, by adjustment factor export to
First module (equivalent to a matrix multiplier and an adder), third unit and Unit the 4th；
Institute's first module, for the result of calculation of the adjustment factor and adaptive algorithm to be carried out into matrix multiple, then
It is added to obtain the first coefficient matrix with the forgetting parameter；
Second unit (equivalent to matrix inverters), for being inverted to obtain the second coefficient to first coefficient matrix
Matrix；
The third unit (equivalent to a matrix multiplier), for the adjustment factor to be multiplied by into the second coefficient square
After battle array, multiplied by with the result of calculation of the adaptive algorithm, the 3rd coefficient matrix is obtained；
Unit the 4th (equivalent to a subtracter and a divider), for the adjustment factor to be subtracted into the 3rd coefficient
After matrix, divided by the forgetting parameter obtains the 4th coefficient matrix；
5th unit (equivalent to accumulator), for by the calculating knot of the 4th coefficient matrix, the adaptive algorithm
Adjustment factor after fruit updated with last time is added up, and obtains the adjustment factor of this renewal.
Wherein, Unit the 4th, it is additionally operable to export the 4th coefficient matrix to second register；
Second register, it is additionally operable to be updated according to the 4th coefficient matrix.
Coefficient adjustment module calculation process may comprise steps of：
Step 201, before coefficient is recalculated every time, first configured the initial value of adjustment factor.
Software configures a representative value, the initial value as adjustment factor according to current application scene.
Step 202, adjustment factor register read in initial value.
Step 203, perform adjustment factor calculating.
First, adjustment factor register all saves the initial value of software merit rating.Renewal is that software is matched somebody with somebody for the first time
Put, updated with the output of divider (afterbody inside adjustment factor calculating) later.Coefficient after renewal is actually
One form, as a matrix (two-dimensional array), then according to algorithm, calculating below is participated in successively.
Adjustment factor calculates specific as follows：
1st, matrix multiplier：According to algorithm, the result of calculation of the adjustment factor after renewal and original adaptive algorithm is entered
Row matrix multiplication；
2nd, adder：It is still (matrix) two-dimensional array after matrix multiplication, is added to obtain first with parameter is forgotten
Coefficient matrix；
3rd, matrix inverters：Inversion operation is carried out to the first coefficient matrix and obtains the second coefficient matrix；
4th, matrix multiplier：After the adjustment factor of adjustment factor register storage is multiplied by second coefficient matrix, multiplied by
With the result of calculation of the adaptive algorithm, the 3rd coefficient matrix is obtained；
5th, subtracter：The adjustment factor of adjustment factor register storage subtracts the 3rd coefficient matrix；
6th, divider：The result divided by forgetting parameter of subtracter output obtain the 4th coefficient matrix, by the 4th coefficient matrix
Accumulator is given all the way, and return all the way is given adjustment factor register and is updated.
7th, accumulator：The value that current accumulator is stored, (i.e. divider exports with adjustment factor calculating section input value
The 4th coefficient matrix) and the result of calculation of adaptive algorithm these three values added up, calculate optimal adjustment factor,
Give coefficient cache module after updating.
Step 204, renewal adjustment factor register.
Repeat 203 to 204 steps.
According to above-mentioned narration, adjustment factor calculating section is a closed-loop control, because this part is that hardware calculates, so
Speed is fast, can reduce the time of stabilization after renewal coefficient rapidly.
Using the system and method for the embodiment of the present invention, performance can be effectively improved, and after reducing system update coefficient,
The stable time.
One of ordinary skill in the art will appreciate that all or part of step in the above method can be instructed by program
Related hardware is completed, and described program can be stored in computer-readable recording medium, such as read-only storage, disk or CD
Deng.Alternatively, all or part of step of above-described embodiment can also be realized using one or more integrated circuits.Accordingly
Ground, each module/unit in above-described embodiment can be realized in the form of hardware, can also use the shape of software function module
Formula is realized.The present invention is not restricted to the combination of the hardware and software of any particular form.
The preferred embodiments of the present invention are these are only, certainly, the present invention can also there are other various embodiments, without departing substantially from this
In the case of spirit and its essence, those skilled in the art work as can make various corresponding changes according to the present invention
And deformation, but these corresponding changes and deformation should all belong to the protection domain of appended claims of the invention.
Claims (6)
1. a kind of method of pre-distortion, including：
In advance to forget the initial value of parameter configuration one, the first register is stored in；
The forgetting parameter that first register exports is multiplied with zoom factor, the difference of the zoom factor is then subtracted with 1
It is added, obtains new forgetting parameter；
By the result of calculation of the adjustment factor of the second register cache and adaptive algorithm carry out matrix multiple, then with the something lost
Forget parameter to be added to obtain the first coefficient matrix；
First coefficient matrix is inverted to obtain the second coefficient matrix；
After the adjustment factor of the second register storage is multiplied by second coefficient matrix, multiplied by with the adaptive algorithm
Result of calculation, obtain the 3rd coefficient matrix；
After the adjustment factor of second register storage subtracts the 3rd coefficient matrix, divided by the forgetting parameter obtains the
Four coefficient matrixes；
Adjustment factor after the result of calculation of 4th coefficient matrix, the adaptive algorithm was updated with last time is tired out
Add, obtain the adjustment factor of this renewal；
The data exported according to an adjustment factor to predistorter carry out pre-distortion.
2. the method as described in claim 1, it is characterised in that：It is described obtain new forgetting parameter after also include：
First register is updated using the new forgetting parameter.
3. the method as described in claim 1, it is characterised in that：It is described to obtain also including after the 4th coefficient matrix：
Second register is updated according to the 4th coefficient matrix.
4. a kind of system of pre-distortion, including：
Processing module, the data for being exported according to an adjustment factor to predistorter carry out pre-distortion；
Coefficient adjustment module, forget adjustment factor described in parameter adjustment for the result of calculation according to adaptive algorithm and one；Its
In, the coefficient adjustment module includes：
Second register, for the adjustment factor of caching, adjustment factor is exported single to first module, third unit and the 4th
Member；
Institute's first module, for the result of calculation of the adjustment factor and adaptive algorithm to be carried out into matrix multiple, then with institute
Forgetting parameter is stated to be added to obtain the first coefficient matrix；
Second unit, for being inverted to obtain the second coefficient matrix to first coefficient matrix；
The third unit, after the adjustment factor is multiplied by into second coefficient matrix, multiplied by with the adaptive calculation
The result of calculation of method, obtain the 3rd coefficient matrix；
Unit the 4th, after the adjustment factor is subtracted into the 3rd coefficient matrix, divided by the forgetting parameter obtains the
Four coefficient matrixes；
Unit the 5th, for the tune after the result of calculation of the 4th coefficient matrix, the adaptive algorithm was updated with last time
Section coefficient is added up, and obtains the adjustment factor of this renewal；
Parameter module is forgotten, for adjusting the forgetting parameter according to a preset zoom factor；Wherein, the forgetting parameter mould
Block includes：
First register, forget parameter for caching, caching is the initial value of the forgetting parameter configuration in advance, by the forgetting
Parameter is exported to adjustment unit and coefficient adjustment module；
Adjustment unit, the forgetting parameter for first register to be exported are multiplied with the zoom factor, then subtracted with 1
The difference addition of the zoom factor, obtains new forgetting parameter, the new forgetting parameter is exported to first register.
5. system as claimed in claim 4, it is characterised in that：
First register, it is additionally operable to be updated using the new forgetting parameter of adjustment unit output.
6. system as claimed in claim 4, it is characterised in that：
Unit the 4th, it is additionally operable to export the 4th coefficient matrix to second register；
Second register, it is additionally operable to be updated according to the 4th coefficient matrix.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101753105A (en) * | 2008-12-18 | 2010-06-23 | 富士通株式会社 | Distortion compensation apparatus and method |
| CN102413085A (en) * | 2011-10-12 | 2012-04-11 | 中兴通讯股份有限公司 | Digital pre-distortion method and device |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101753105A (en) * | 2008-12-18 | 2010-06-23 | 富士通株式会社 | Distortion compensation apparatus and method |
| CN102413085A (en) * | 2011-10-12 | 2012-04-11 | 中兴通讯股份有限公司 | Digital pre-distortion method and device |
Non-Patent Citations (1)
| Title |
|---|
| Predistortion of Hammerstein and Wiener systems using the Nonlinear Filtered-x Prediction Error Method algorithm;E. Abd-Elrady, L. Gan, G. Kubin;《Elektrotechnik & Informationstechnik》;Springer-Verlag;20101031;第285-288页 * |
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