CN106411271A - Predistortion device for power amplifier and parameter selection method thereof - Google Patents
Predistortion device for power amplifier and parameter selection method thereof Download PDFInfo
- Publication number
- CN106411271A CN106411271A CN201610652841.XA CN201610652841A CN106411271A CN 106411271 A CN106411271 A CN 106411271A CN 201610652841 A CN201610652841 A CN 201610652841A CN 106411271 A CN106411271 A CN 106411271A
- Authority
- CN
- China
- Prior art keywords
- power amplifier
- predistortion
- memory
- signal
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010187 selection method Methods 0.000 title abstract description 9
- 239000013598 vector Substances 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 21
- 239000011159 matrix material Substances 0.000 claims description 45
- 238000012545 processing Methods 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 6
- 230000003446 memory effect Effects 0.000 description 21
- 230000000694 effects Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 230000006399 behavior Effects 0.000 description 7
- 238000003199 nucleic acid amplification method Methods 0.000 description 6
- 230000003321 amplification Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000009022 nonlinear effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03F—AMPLIFIERS
- H03F1/00—Details of amplifiers with only discharge tubes, only semiconductor devices or only unspecified devices as amplifying elements
- H03F1/32—Modifications of amplifiers to reduce non-linear distortion
- H03F1/3241—Modifications of amplifiers to reduce non-linear distortion using predistortion circuits
- H03F1/3258—Modifications of amplifiers to reduce non-linear distortion using predistortion circuits based on polynomial terms
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/38—Synchronous or start-stop systems, e.g. for Baudot code
- H04L25/40—Transmitting circuits; Receiving circuits
- H04L25/49—Transmitting circuits; Receiving circuits using code conversion at the transmitter; using predistortion; using insertion of idle bits for obtaining a desired frequency spectrum; using three or more amplitude levels ; Baseband coding techniques specific to data transmission systems
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Algebra (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Nonlinear Science (AREA)
- Power Engineering (AREA)
- Amplifiers (AREA)
Abstract
The invention discloses a predistortion device for a power amplifier and a parameter selection method thereof. According to the device and the method, the analog fitting degree of a power amplifier back inverse model is high, and the coefficient resolving precision of a predistorter is high. The device comprises a predistortion model operation processor which is used for establishing a predistortion model expression based on a memory polynomial, wherein the order of each memory item in the memory polynomial is determined by a vector parameter corresponding to the memory item in a first vector parameter group; and the predistorter which is used for receiving an input signal and obtaining an output signal according to the predistortion model expression.
Description
Technical Field
The invention relates to the technical field of electronics, in particular to a predistortion device for a power amplifier and a parameter selection method thereof.
Background
Power amplifiers, i.e., power amplifiers, are important components of communication systems and are also one of the main nonlinear devices. Due to the nonlinear characteristic of the power amplifier, the out-of-band spectrum spreading of a transmission signal causes adjacent channel interference, and the in-band amplitude and phase are distorted, so that the error rate is increased. In order to overcome the nonlinear characteristic of the power amplifier, a backspacing method can be adopted to make the operating point of the power amplifier backspace and only work in a linear region. But the efficiency of such power amplifiers is very low. For example, in the field of wireless communication systems and radio and television communication, in order to increase the coverage area and simultaneously take power amplifier efficiency into consideration, signal peak values often work near power amplifiers P-1 to P-3dB, and signal distortion, out-of-band distortion and the like are caused by serious power amplifier nonlinearity.
The other method is to make the power amplifier still work in the non-linear region, but before the input signal enters the power amplifier, the input signal passes through a predistorter and then the power amplifier. The predistorter is also a nonlinear device, but the nonlinear characteristic of the predistorter is just opposite to that of the power amplifier, so that the output signal and the input signal which are obtained after the input signal undergoes two times of nonlinear changes present a linear relation. For example, the digital predistortion technology can correct the nonlinear characteristic of a power amplifier, so that the output signal is linearized, and a better efficiency index is obtained.
However, the power amplifier has a memory effect for a wide band signal in addition to a nonlinear characteristic. When the bandwidth of the input signal is far smaller than the bandwidth of the power amplifier, the memory effect of the power amplifier can be ignored; when the input signal has a wide bandwidth, the memory effect cannot be ignored, and the output of the power amplifier is not only related to the current input but also related to the previous input.
In the prior art, a traditional memory polynomial model is usually used as a behavior model of a power amplifier and a predistorter to perform predistortion processing, for example, the output of the predistorter can be expressed as:
in the formula (1), x (n) and y (n) respectively represent input and output signals of the predistorter, a is a predistorter parameter, K is the order of the memory polynomial, and Q is the memory depth of the memory polynomial.
However, although the inverse model after power amplification can be better simulated due to the large values of K and Q, when solving the predistorter coefficient of the inverse model after power amplification, the problem that the solving accuracy of the predistorter coefficient is reduced due to the overlarge data matrix is encountered; and the values of K and Q are too small, so that the precision of solving the coefficient of the predistorter can be improved, but the problem of poor fitting effect can be caused by the reduction of the complexity of a predistortion model.
Disclosure of Invention
At least one of the objectives of the present invention is to provide a predistortion apparatus for a power amplifier and a parameter selection method thereof, which have high inverse model simulation fitting degree after power amplification and high predistorter coefficient solving precision, in view of the above problems in the prior art.
In order to achieve the purpose, the invention adopts the technical scheme that:
a predistortion apparatus for a power amplifier, comprising:
the predistortion model operation processor is used for establishing a predistortion model expression based on a memory polynomial; the order of each memory term in the memory polynomial is determined by a vector parameter corresponding to the memory term in the first vector parameter group; and the number of the first and second groups,
and the predistorter is used for receiving the input signal and acquiring an output signal according to the predistortion model expression.
Preferably, the predistortion model expression based on the memory polynomial is:
wherein x (n) is an input signal, y (n) is an output signal, A is a predistortion model coefficient, K is an order of a memory polynomial, Q is a memory depth of the memory polynomial, n is a positive integer, qn is Q-1, and the first vector parameter group (K) is a vector parameter group (K) with a constant value0，k1，k2，k3…kqn)∈[1，2，3…K]。
Preferably, the predistortion model expression based on the memory polynomial is:
wherein qdly is the memory depth step value of the memory polynomial.
Preferably, the memory depth step value qdly ∈ {1, 2, 3} of the memory polynomial is set.
Preferably, the predistortion apparatus further comprises: and the power amplifier back inverse model coefficient operation processor is used for acquiring the back inverse model coefficient of the power amplifier according to the power amplifier forward signal and the feedback signal.
A parameter selection method for a predistortion apparatus, comprising:
sending a calibration sequence digital signal to obtain a power amplifier feedback signal; determining a first set of parameter sets of a predistortion model; acquiring a post-inverse model coefficient matrix of the power amplifier; acquiring the characteristic quantity of each parameter group according to the inverse model coefficient matrix of the power amplifier and each parameter group in the first parameter group set; and selecting the parameter group with the minimum characteristic quantity value as the parameter group of the predistortion model.
Preferably, the determining the first parameter set of the predistortion model includes:
the order of the memory polynomial after the order reduction according to the first vector parameter group and a parameter group consisting of corresponding memory depths; or,
and the parameter group is composed of the order of the memory polynomial after the order reduction according to the first vector parameter group and the corresponding memory depth after the memory depth stepping value of the memory polynomial is increased.
Preferably, the obtaining of the inverse model coefficient matrix of the power amplifier includes:
aligning the power amplifier forward signal and the power amplifier feedback signal to obtain a processed forward signal and a processed feedback signal; generating a feedback data matrix according to the processed feedback signal; generating a forward data matrix according to the processed forward signal; and acquiring a backward inverse model coefficient matrix of the power amplifier according to the feedback data matrix and the forward data matrix.
Preferably, the obtaining the characteristic quantity of each parameter group according to the inverse model coefficient matrix of the power amplifier and each parameter group in the first parameter group set includes:
establishing a predistortion model expression according to the inverse model coefficient matrix of the power amplifier and each parameter set in the first parameter set; inputting the processed feedback signals into a predistorter corresponding to each parameter group respectively, and performing predistortion processing by the predistorter according to a corresponding predistortion model expression to obtain a post-inverse predistortion signal corresponding to each parameter group; and calculating the minimum mean square error value of the post inverse pre-distortion signal and the processed forward signal corresponding to each parameter set as the characteristic quantity of each parameter set.
Preferably, the obtaining a coefficient matrix of a backward inverse model of the power amplifier according to the feedback data matrix and the forward data matrix includes: and obtaining a backward inverse model coefficient matrix of the power amplifier by a least square method.
In summary, due to the adoption of the technical scheme, the invention at least has the following beneficial effects:
1. the order of each memory item in the memory polynomial is determined by one vector parameter corresponding to the memory item in one vector parameter group, and the order of each memory item is not determined uniformly and is identical to the order of the memory polynomial, so that the predistortion model can be more accurately close to the model of an actual power amplifier, the nonlinear performance of different power amplifiers in different working environments can be more accurately matched, and the purpose of linearization of the power amplifier is further achieved;
2. the order of the memory term can be configured into a lower order lower than that of the memory polynomial according to an actual application scene, so that the scale of a data matrix can be reduced, and the solving precision is increased; under the condition that the complexity of the predistortion model is kept unchanged, the fitting difficulty of the power amplifier memory effect and the corresponding high-order polynomial can be reduced, and the fitting effect on the power amplifier is improved
3. By introducing the memory depth stepping value of the polynomial, the length of the memory effect can be increased under the condition of not increasing the scale of the data matrix, so that the fitting effect on the memory effect of the power amplifier is enhanced, and the matching degree of the representation of the memory effect of the power amplifier and the actual behavior of the power amplifier is improved.
Drawings
Fig. 1 is a schematic structural diagram of a predistortion apparatus for a power amplifier according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a predistortion apparatus for a power amplifier according to a fourth embodiment of the present invention;
fig. 3 is a flowchart of a parameter selection method for a predistortion apparatus according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and embodiments, so that the objects, technical solutions and advantages of the present invention will be more clearly understood. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example one
As shown in fig. 1, a predistortion apparatus for a power amplifier disclosed in an embodiment of the present invention includes:
the predistortion model operation processor is used for establishing a predistortion model expression based on a memory polynomial; wherein the order of each memory term in the memory polynomial is determined by one vector parameter in the first vector parameter set; and the number of the first and second groups,
the predistorter is used for receiving an input signal x (n) and acquiring an output signal y (n) according to a predistortion model expression established by the predistortion model operation processor.
The order of each memory item in the memory polynomial is determined by one vector parameter corresponding to the memory item in one vector parameter group, and the order of each memory item is not determined uniformly and is identical to the order of the memory polynomial, so that the predistortion model can be more accurately close to the model of an actual power amplifier, the nonlinear performance of different power amplifiers in different working environments can be more accurately matched, and the purpose of linearization of the power amplifier is further achieved.
Example two
The traditional predistortion model expression based on order K and memory depth Q of the memory polynomial can be expressed as:
y(n)＝∑(f1(x(n)),f2(x(n-1)),...fQ(x(n-Q+1)))
wherein x (n) is an input signal, y (n) is an output signal, fnA polynomial of order K is characterized. By introducing a first vector parameter set (k)0，k1，k2，k3…kqn) The order of each memory term in the memory polynomial is determined by a vector parameter corresponding to the memory term in the vector parameter set, fnIs defined according to the vector parameters in the first vector parameter set, i.e. each polynomial is no longer unified as a polynomial of order K, but rather by the vector parameter set (K)0，k1，k2，k3…kqn) A polynomial of control.
Thus, the memory polynomial based predistortion model expression may be expressed as:
where a is a predistortion model coefficient (i.e., a post-inverse model coefficient, which may be obtained by solving a post-inverse model coefficient matrix of a power amplifier described below), n is a positive integer, qn is Q-1, and the first vector parameter group (k) is a set of vectors0，k1，k2，k3…kqn)∈[1，2，3…K]。
By introducing the vector parameter group, the order of each memory term in the memory polynomial is respectively determined by one vector parameter corresponding to the memory term in the vector parameter group, so that the order of the memory term can be configured to be a lower order lower than that of the memory polynomial according to an actual application scene, the scale of a data matrix can be reduced, and the solving precision is increased; under the condition that the complexity of the predistortion model is kept unchanged, the fitting difficulty of the power amplifier memory effect and the corresponding high-order polynomial can be reduced, and the fitting effect on the power amplifier is improved.
EXAMPLE III
The memory effect of the power amplifier is shown in that the current output is not only a function of the current input, but also related to the past input, and the expression can be expressed as:
y(n)＝f(x(n),x(n-1),...x(n-Q+1))
increasing the memory depth Q value of the memory polynomial can improve the simulation of the memory effect degree, increase the scale of the data matrix and reduce the solving precision. However, by introducing the parameter qdly, i.e. the memory depth step value of the polynomial, the memory effect length can be increased, but the scale of the data matrix is not increased. Therefore, the above expression can be expressed as follows after introducing the parameter qdly:
y(n)＝f(x(n),x(n-1·qdly),...x(n-(Q+1)·qdly))
it can be known from the formula that qdly is doubled and the memory effect coverage length is doubled under the condition that the value of Q is not changed, and the complexity of model solution is not increased.
Further, the vector parameter set (k) in the above embodiment is introduced0，k1，k2，k3…kqn) And after the memory depth stepping value qdly of the polynomial, the predistortion model expression based on the memory polynomial can be expressed as:
in a preferred embodiment, when the memory depth step value qdly ∈ {1, 2, 3} of the memory polynomial is closer to the memory effect depth (or length) of the actual power amplifier, the fitting effect on the memory effect of the power amplifier is better.
By introducing the memory depth stepping value of the polynomial, the length of the memory effect can be increased under the condition of not increasing the scale of the data matrix, so that the fitting effect on the memory effect of the power amplifier is enhanced, and the matching degree of the representation of the memory effect of the power amplifier and the actual behavior of the power amplifier is improved.
Example four
As shown in fig. 2, in a preferred embodiment, the predistortion apparatus may further include a post-power-amplifier inverse model coefficient operation processor, configured to obtain a post-inverse model coefficient of the power amplifier according to a forward signal y (n) and a feedback signal z (n) of the power amplifier.
The pre-distortion model coefficient can be determined by fitting a pre-distortion model with actually captured power amplifier forward signals and feedback signals under the working signal system and the power amplifier working environment, and taking parameters corresponding to the selected minimum Mean Square Error (MSE) value as modeling parameters of the pre-distortion model.
The predistortion device in the above embodiment of the present invention may be applied to a system based on an indirect predistortion architecture, in such a system, a predistortion model of the predistortion device is equivalent to a backward inverse model of a power amplifier, and a process of updating a predistortion model coefficient may be obtained by collecting forward and feedback data of the power amplifier. The post-inverse model of the power amplifier is subjected to accurate behavior simulation, so that the linearization capability of the predistortion device for the power amplifier is improved.
EXAMPLE five
The fifth embodiment of the invention provides a parameter selection method for a predistortion device, which can be executed in the initialization process of the predistortion device and also can be executed when the signal system is changed or the power amplifier behavior is changed. The parameter selection process does not affect the normal predistortion operation process, and before a new optimal parameter set is not selected, the predistortion device completes the predistortion process by using the parameter value selected at the previous time. As shown in fig. 3, the parameter selection method specifically includes the following steps:
step 301: sending a calibration sequence digital signal to obtain a power amplifier feedback signal;
the predistorter switches the output signal from the working data signal to the calibration sequence data signal; wherein the bandwidth, peak-to-average ratio, and modulation of the calibration sequence data signal are consistent with the working data signal.
In a preferred embodiment, the calibration data signal may be transmitted during a period of time when no operational data signal or the operational data signal is set to zero, in order that the transmission of the calibration sequence data signal does not affect the normal operational data signal output.
And inputting the calibration sequence data signal (namely a power amplifier forward signal TX) into a power amplifier, capturing a feedback signal output by the power amplifier and subjected to the nonlinear action of the power amplifier, and acquiring a power amplifier feedback signal RX.
Step 302: determining a first set of parameter sets of a predistortion model;
this step may be performed simultaneously with, before or after the preceding step;
in particular, it may be determined to include n sets of parameter sets enS ═ e, the first set of parameters1,e2,e3...enI.e. to determine which alternative sets of parameters are to be evaluated. The first set of parameter sets S may include all selectable sets of parameter sets of the memory polynomial parameter sets (K, Q).
In a preferred embodiment, the first set of parameter sets S may comprise parameters (k) according to a first vector parameter set0，k1，k2，k3…kqn) The order of the memory polynomial after the reduction, and the parameter set ((k) composed of the corresponding memory depth0，k1，k2，k3…kqn) Q), wherein qn ═ Q-1; or further comprises a parameter group ((k) composed of corresponding memory depths after increasing the memory depth step value of the memory polynomial0，k1，k2，k3…kqn) Qdly q), wherein qdly ∈ {1, 2, 3 … }.
The parameter set may include a free combination of all optional parameters, or may be a subset thereof. The number of specific parameter sets may be determined by various application scenarios and signal characteristics, respectively. Since the size n of the set affects the time consumption of the preferred algorithm, all possible sets of parameters can be enumerated, without being sensitive to the preferred time. For example, when K is 7 and Q is 5, the total number of elements in the parameter set is 7^ theoretically5. However, in practical applications, the parameter set is (1, 1, 1, 1, 1) or the like, which has a very limited effect on fitting the post-power-amplification inverse model, and therefore, the parameter set that is practically usable is usually a model parameter such as (7, 7, 5, 3, 1) or the like, which has a reduced order in terms with a deep memory effect. Preferably, the value range of the parameter qdly is {1, 2, 3}, so that the situation that the value exceeds the actual power amplifier due to overlarge value can be avoidedThe length of the memory effect is increased, so that the fitting effect of the inverse model after power amplification is enhanced.
Step 303: acquiring a post-inverse model coefficient matrix of the power amplifier;
specifically, the alignment processing is carried out on a power amplifier forward signal and a power amplifier feedback signal; for example, time alignment, power alignment and phase alignment are performed on a power amplifier forward signal TX and a feedback signal RX, and a processed forward signal TXD and a processed feedback signal RXD are obtained;
the inverse model coefficient operation processor after power amplification generates a feedback data matrix X according to the processed feedback signal RXDRXDGenerating a forward data matrix Y according to the processed forward signal TXD, and solving a backward inverse model mathematical expression for representing the power amplifier, namely an equation Am·XRXDObtaining a post inverse model coefficient matrix A of the power amplifier as Ym. The equation is an over-determined equation, and in a preferred embodiment, the coefficient matrix A may be obtained by solving a Least squares solution using an LS (Least squares) algorithmm。
The steps can be executed for multiple times according to the application scene so as to obtain the backward inverse model coefficient matrix with higher fitting degree with the power amplifier.
Step 304: acquiring the characteristic quantity of each parameter group according to the inverse model coefficient matrix of the power amplifier and each parameter group in the first parameter group set;
establishing a predistortion model expression according to the inverse model coefficient matrix of the power amplifier and each parameter set in the first parameter set; inputting the processed feedback signals into a predistorter corresponding to each parameter group respectively, and performing predistortion processing by the predistorter according to a corresponding predistortion model expression to obtain a post-inverse predistortion signal corresponding to each parameter group; calculating the minimum mean square error value of the post-inverse pre-distortion signal and the processed forward signal corresponding to each parameter set as the characteristic quantity of each parameter set;
specifically, the predistortion model operation processor is based onPower amplifier back inverse model coefficient matrix AmEstablishing a predistortion model expression for each parameter set in the first parameter set, inputting the processed feedback signal RXD into a predistorter corresponding to the processed feedback signal RXD, and performing predistortion processing by the predistorter according to the predistortion model expression to obtain a post-inverse predistortion signal RXD _ PINV;
further, an MSE value err of the post-inverse pre-distortion signal RXD _ PINV and the processed forward signal TXD is calculatedmAs the evaluation parameter set S ═ { e ═ e%1,e2,e3...enParameter set e in (1) }m|m∈[1,2,3..n]Respectively obtaining corresponding characteristic quantities errm|m∈[1,2,3..n]. Characteristic quantity errmCharacterizing a parameter set emFor the approximation degree of the fitting of the actual inverse model after the power amplifier, the smaller the characteristic quantity value is, the smaller the fitting error is.
Step 305: selecting the parameter group with the minimum characteristic quantity value as the parameter group of the predistortion model;
specifically, the parameter set with the minimum characteristic quantity value may be selected as the optimal parameter set ek|errk＝min{errm|m∈[1,2,3...n]}。
In a preferred embodiment, the method can further comprise the following steps: monitoring whether the signal state changes in real time, for example, when the bandwidth, modulation mode, power of the working signal or the power amplifier changes, executing step 301 to perform parameter selection again, so that the memory effect performance of a new signal under the power amplifier can be matched; alternatively, each step of the parameter selection may be performed at intervals of a preset time period according to the characteristics of the operating signal.
In the above embodiment, if the signal format, the output power, and the power amplifier are modified, the parameter set optimization process may be restarted, but before the parameter optimization process completes determining a new preferred parameter set, the parameter set optimization process continues to perform the pre-distortion processing using the previous preferred parameter set. And after the new optimal parameter group is determined, performing periodic predistortion treatment on the new optimal parameter group to determine the optimal parameter group of the power amplifier inverse model which is fitted in a new environment.
By the method, the optimal pre-distortion model modeling parameter set can be determined, so that the pre-distortion device can simulate the inverse behavior of the power amplifier optimally, and particularly, the matching degree of the characterization of the memory effect of the power amplifier and the actual behavior of the power amplifier can be the highest. In practical application, the predistortion device optimized by parameters has the strongest capability of linearizing the power amplifier, and specific radio frequency indexes are embodied on the improvement of parameters such as ACLR (Adjacent Channel Leakage Ratio), EVM (error vector magnitude), and the like.
The above embodiments are only for illustrating the preferred embodiments of the present invention and not for limiting the present invention. Various alterations, modifications and improvements will occur to those skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. A predistortion apparatus for a power amplifier, the predistortion apparatus comprising:
the predistortion model operation processor is used for establishing a predistortion model expression based on a memory polynomial; the order of each memory term in the memory polynomial is determined by a vector parameter corresponding to the memory term in the first vector parameter group; and the number of the first and second groups,
and the predistorter is used for receiving the input signal and acquiring an output signal according to the predistortion model expression.
2. The predistortion device according to claim 1, wherein the predistortion model expression based on memory polynomial is:
wherein x (n) is an input signal, y (n) is an output signal, A is a predistortion model coefficient, K is an order of a memory polynomial, Q is a memory depth of the memory polynomial, n is a positive integer, qn is Q-1, and the first vector parameter group (K) is a vector parameter group (K) with a constant value0，k1，k2，k3…kqn)∈[1，2，3…K]。
3. The predistortion device according to claim 2, wherein the predistortion model expression based on memory polynomial is:
wherein qdly is the memory depth step value of the memory polynomial.
4. A predistortion arrangement according to claim 3, characterized in that the memory depth step value qdly e {1, 2, 3} of the memory polynomial.
5. The predistortion apparatus according to any one of claims 2 to 4, characterized in that the predistortion apparatus further comprises: and the power amplifier back inverse model coefficient operation processor is used for acquiring the back inverse model coefficient of the power amplifier according to the power amplifier forward signal and the feedback signal.
6. A method for parameter selection for a predistortion device, the method comprising:
sending a calibration sequence digital signal to obtain a power amplifier feedback signal; determining a first set of parameter sets of a predistortion model; acquiring a post-inverse model coefficient matrix of the power amplifier; acquiring the characteristic quantity of each parameter group according to the inverse model coefficient matrix of the power amplifier and each parameter group in the first parameter group set; and selecting the parameter group with the minimum characteristic quantity value as the parameter group of the predistortion model.
7. The method of claim 6, wherein determining the first set of parameter sets for the predistortion model comprises:
the order of the memory polynomial after the order reduction according to the first vector parameter group and a parameter group consisting of corresponding memory depths; or,
and the parameter group is composed of the order of the memory polynomial after the order reduction according to the first vector parameter group and the corresponding memory depth after the memory depth stepping value of the memory polynomial is increased.
8. The method of claim 6, wherein the obtaining the inverse model coefficient matrix of the power amplifier comprises:
aligning the power amplifier forward signal and the power amplifier feedback signal to obtain a processed forward signal and a processed feedback signal; generating a feedback data matrix according to the processed feedback signal; generating a forward data matrix according to the processed forward signal; and acquiring a backward inverse model coefficient matrix of the power amplifier according to the feedback data matrix and the forward data matrix.
9. The method of claim 8, wherein the obtaining the characteristic quantity of each parameter set according to the inverse model coefficient matrix of the power amplifier and each parameter set in the first parameter set comprises:
establishing a predistortion model expression according to the inverse model coefficient matrix of the power amplifier and each parameter set in the first parameter set; inputting the processed feedback signals into a predistorter corresponding to each parameter group respectively, and performing predistortion processing by the predistorter according to a corresponding predistortion model expression to obtain a post-inverse predistortion signal corresponding to each parameter group; and calculating the minimum mean square error value of the post inverse pre-distortion signal and the processed forward signal corresponding to each parameter set as the characteristic quantity of each parameter set.
10. The method of claim 8, wherein obtaining a backward inverse model coefficient matrix of the power amplifier according to the feedback data matrix and the forward data matrix comprises: and obtaining a backward inverse model coefficient matrix of the power amplifier by a least square method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610652841.XA CN106411271B (en) | 2016-08-10 | 2016-08-10 | A kind of pre-distortion device and its parameter selection method for power amplifier |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610652841.XA CN106411271B (en) | 2016-08-10 | 2016-08-10 | A kind of pre-distortion device and its parameter selection method for power amplifier |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106411271A true CN106411271A (en) | 2017-02-15 |
| CN106411271B CN106411271B (en) | 2019-04-12 |
Family
ID=58004193
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610652841.XA Active CN106411271B (en) | 2016-08-10 | 2016-08-10 | A kind of pre-distortion device and its parameter selection method for power amplifier |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106411271B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274752A (en) * | 2018-12-05 | 2020-06-12 | 北京大学 | Power amplifier behavior modeling method based on two-stage open-loop structure and binary function unit |
| CN113630091A (en) * | 2020-05-08 | 2021-11-09 | 大唐移动通信设备有限公司 | Power amplifier and predistortion model generation method and device thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102970262A (en) * | 2012-11-16 | 2013-03-13 | 华南理工大学 | Method for improving digital pre-distortion stability |
| CN104320093A (en) * | 2014-10-08 | 2015-01-28 | 中国科学院上海高等研究院 | System and method for stabilizing amplifier |
| CN105262447A (en) * | 2015-11-26 | 2016-01-20 | 中国电子科技集团公司第三十研究所 | Pre-distortion method and device for power amplifier and radio frequency system |
-
2016
- 2016-08-10 CN CN201610652841.XA patent/CN106411271B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102970262A (en) * | 2012-11-16 | 2013-03-13 | 华南理工大学 | Method for improving digital pre-distortion stability |
| CN104320093A (en) * | 2014-10-08 | 2015-01-28 | 中国科学院上海高等研究院 | System and method for stabilizing amplifier |
| CN105262447A (en) * | 2015-11-26 | 2016-01-20 | 中国电子科技集团公司第三十研究所 | Pre-distortion method and device for power amplifier and radio frequency system |
Non-Patent Citations (1)
| Title |
|---|
| 于翠屏: "宽带功率放大器预失真技术的研究", 《中国博士学位论文全文数据库信息科技辑（月刊）》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274752A (en) * | 2018-12-05 | 2020-06-12 | 北京大学 | Power amplifier behavior modeling method based on two-stage open-loop structure and binary function unit |
| CN111274752B (en) * | 2018-12-05 | 2022-05-03 | 北京大学 | Power amplifier behavior modeling method based on two-stage open loop and binary function |
| CN113630091A (en) * | 2020-05-08 | 2021-11-09 | 大唐移动通信设备有限公司 | Power amplifier and predistortion model generation method and device thereof |
| CN113630091B (en) * | 2020-05-08 | 2024-03-29 | 大唐移动通信设备有限公司 | Power amplifier and predistortion model generation method and device thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106411271B (en) | 2019-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20230370937A1 (en) | Method and system for baseband predistortion linearization in multi-channel wideband communication systems | |
| US10523159B2 (en) | Digital compensator for a non-linear system | |
| JP5753272B2 (en) | Nonlinear model with tap output normalization | |
| CN103299542B (en) | For the orthogonal basis function collection of digital predistorter | |
| US8787494B2 (en) | Modeling digital predistorter | |
| TWI326966B (en) | Apparatus and method of dynamically adapting the lut spacing for linearizing a power amplifier | |
| KR101389880B1 (en) | Apparatus and method for low cost implementation of adaptive digital predistortion algorithm using envelope detection feedback | |
| WO2015096735A1 (en) | Digital pre-distortion parameter obtaining method and pre-distortion system | |
| KR101679230B1 (en) | Polynomial digital predistortion apparatus for compensation of non-linear characteristic of power amplifier and the method thereof | |
| JP6010422B2 (en) | Method, system, and computer-readable medium for predistorting a signal with respect to a non-linear component in the presence of a long-term memory effect | |
| CN103856429A (en) | Adaptive predistortion system and method based on hybrid indirect learning algorithm | |
| CN105471784A (en) | Digital predistortion method of jointly compensating for IQ imbalance and PA non-linearity | |
| Rahati Belabad et al. | An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure | |
| CN102611661B (en) | Predistortion device and method based on precise inverse solution memory polynomial model equation | |
| CN106411271B (en) | A kind of pre-distortion device and its parameter selection method for power amplifier | |
| Suryasarman et al. | Optimizing the identification of digital predistorters for improved power amplifier linearization performance | |
| CN115913140B (en) | Piecewise polynomial digital predistortion device and method for controlling operation precision | |
| Safari et al. | Spline-based model for digital predistortion of wide-band signals for high power amplifier linearization | |
| CN108111448A (en) | Generation method, device and the pre-distortion calibration equipment of predistortion lookup table | |
| Smirnov | Cascaded Model of Nonlinear Operator for Digital Predistortion with Memory | |
| Aschbacher et al. | Prototype implementation of two efficient low-complexity digital predistortion algorithms | |
| Santucci et al. | Estimation for amplify-and-forward transmissions with nonlinear amplifiers | |
| Xu et al. | A Robust Estimation Method for Nonlinear Model Coefficients Using Ridge Regression | |
| Ambagahawela Rathnayake Mudiyanselage | Augmented-LSTM and 1D-CNN-LSTM based DPD models for linearization of wideband power amplifiers | |
| Kaur et al. | Analysis and Simulations of the Effects of Non-linearity on the Radio Frequency Power Amplifier Modelling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20181206 Address after: 610041 High-tech Incubation Park Building 6, North Tianfu Avenue, Chengdu High-tech Zone, Sichuan Province Applicant after: Chengdu core software Co., Ltd. Address before: 610041 Information Security Base of Tianfu Avenue High-tech Incubation Park, Chengdu High-tech Zone, Sichuan Province, 3rd and 4th Floors Applicant before: Chengdu NTS Technology Co., Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |