CN117875246A - Modeling method for power amplifier broadband nonlinear behavior model containing memory effect - Google Patents
Modeling method for power amplifier broadband nonlinear behavior model containing memory effect Download PDFInfo
- Publication number
- CN117875246A CN117875246A CN202410275153.0A CN202410275153A CN117875246A CN 117875246 A CN117875246 A CN 117875246A CN 202410275153 A CN202410275153 A CN 202410275153A CN 117875246 A CN117875246 A CN 117875246A
- Authority
- CN
- China
- Prior art keywords
- power amplifier
- behavior model
- model
- parameters
- function
- 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
- 230000006399 behavior Effects 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000003446 memory effect Effects 0.000 title claims abstract description 16
- 230000006870 function Effects 0.000 claims description 47
- 230000008569 process Effects 0.000 claims description 6
- 238000012892 rational function Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 abstract description 3
- 238000004088 simulation Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000009022 nonlinear effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000002939 conjugate gradient method Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000002945 steepest descent method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/373—Design optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Amplifiers (AREA)
Abstract
The invention discloses a modeling method of a power amplifier broadband nonlinear behavior model comprising a memory effect. The model has the capability of simulating the in-band fluctuation characteristic of the high-frequency broadband carrier wave, greatly widens the applicable scene of the model, has the advantages of good universality, convenient parameter extraction and the like, and provides an efficient means for simulating, designing and optimizing the broadband high-frequency communication, radar, electronic war and other systems.
Description
Technical Field
The invention relates to the technical field of electronic circuit modeling, in particular to a modeling method of a power amplifier broadband nonlinear behavior model containing a memory effect.
Background
The power amplifier is widely focused as a key device in an electronic system, and the behavior model is mainly applied to simulation and design of circuits and systems. Compared with a transistor amplifier model, the power amplifier behavior model has the advantages of high simulation speed and no need of knowing the internal information of the power amplifier circuit in advance during modeling; the inherent nonlinear effects introduced by the active devices and memory effects introduced by bias networks, trap networks and thermal effects are considered in modeling.
At present, most of the modeling methods are used for establishing a power amplifier behavior model by adopting an envelope tracking technology, and the modeling method is effective only when a low-frequency narrow-band power amplifier is used, namely, the absolute bandwidth of a modulation signal is relatively effective when the absolute bandwidth of the modulation signal is relatively small (a plurality of MHz). However, for a high-frequency broadband power amplifier with an absolute bandwidth of a modulation signal reaching several GHz or a millimeter wave power amplifier for broadband high-speed data transmission and broadband electronic countermeasure, the carrier in-band fluctuation characteristic of the high-frequency broadband power amplifier needs to be considered, and for the power amplifier, a power amplifier behavior model based on a low-frequency envelope does not have the capability of simulating the in-band fluctuation characteristic of a high-frequency broadband carrier, and therefore, the high-frequency broadband modulation signal cannot be simulated and cannot be applied to the power amplifier. Therefore, the bandpass characteristics of the high-frequency carrier wave of the power amplifier need to be considered in establishing the high-frequency broadband power amplifier behavior model.
Aiming at the problems existing in the current power amplifier behavior model modeling, a behavior model modeling method capable of representing the wideband nonlinearity and memory effect of the power amplifier is needed to be applied to simulation, design and optimization of a high-frequency wideband radio frequency system.
Disclosure of Invention
The invention aims to provide a modeling method of a power amplifier broadband nonlinear behavior model containing a memory effect, and the power amplifier behavior model established by the modeling method can accurately represent the memory effect and the nonlinear effect of a high-frequency broadband power amplifier, is suitable for the high-frequency broadband power amplifier with an absolute bandwidth of a modulation signal reaching several GHz or a millimeter wave power amplifier for broadband high-speed data transmission and broadband electronic countermeasure.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a modeling method of a power amplifier broadband nonlinear behavior model containing memory effect comprises the following steps:
step 1: establishing an initial power amplifier behavior model;
step 2: acquiring a discrete value of each working frequency point of the initial power amplifier behavior model in a working frequency band;
firstly, determining each working frequency point of a power amplifier behavior model in a working frequency band;
in the method, in the process of the invention,for the ith operating frequency point of the power amplifier behavior model within the operating frequency band,as a starting point for the operating frequency band of the power amplifier behavior model,for the end of the operating frequency band of the power amplifier behavior model,for the uniform sampling total number of the working frequency bands, i=1, 2,3, … and N f ；
Then, measuring the input-output characteristics of the power amplifier behavior model at each operating frequency point;
applying an input signal to an input of an initial power amplifier working model and measuring an output signal amplitude of an output of the initial power amplifier working model;
finally, constructing a nonlinear equation according to the input-output characteristics of the measured power amplifier behavior model at each working frequency point, and solving the nonlinear equation to obtain parameters a, b, c, d, h m (m=0, 1,2, …, M), obtaining a discrete value of each operating frequency point of the power amplifier behavior model in the operating frequency band;
step 3: fitting the parameters a, b, c, d, h obtained in step 2 by a fitting function m (m=0, 1,2, …, M) discrete values of each operating frequency point in the operating frequency band, looking at the relationship of the power amplifier behavior model with the frequency variation;
step 4: will beParameters a, b, c, d, h of step 3 m The fitting function of (m=0, 1,2, …, M) is replaced into the initial power amplifier behavior model to obtain the final power amplifier behavior model.
Further, step 1 adopts a Hammerstein-Weiner model to build an initial power amplifier behavior model:
where y (t) is the output signal of the power amplifier, a, b, c, d, h m As a parameter of the power amplifier behavior model,x(t-mτ) In a power amplifiert-mτThe input signal at the moment τ is the memory interval, M is the memory length, m=0, 1,2, …, M.
Further, the nonlinear equation is constructed in the step 2 as follows:
in I y j (t) is the output signal amplitude of the power amplifier behavior model, |x j (t-mτ) I is the behavior model of the power amplifier int-mτInput signal at time j=1, 2,3, …, N Mag ，N Mag For scanning point number in working frequency band of input signal a, b, c, d, h m τ is a memory interval, M is a memory length, m=0, 1,2, …, M, which is a parameter of the power amplifier behavior model.
Further, the fitting function in step 3 may be one of a polynomial function, a rational function, a sine function, a cosine function, and a spline function.
Further, the power amplifier behavior model parameters a, b, c, d, h m The fitting function of (m=0, 1,2, …, M) is as follows:
in the method, in the process of the invention,a、b、c、d、h m （m=0，1，2，…，M) A is a parameter of a power amplifier behavior model ak 、B ak 、C ak Model parameters for power amplifier behavioraParameters of the fitting function, A bk 、B bk 、C bk Model parameters for power amplifier behaviorbParameters of the fitting function, A ck 、B ck 、C ck Model parameters for power amplifier behaviorcParameters of the fitting function, A dk 、B dk 、C dk Model parameters for power amplifier behaviordParameters of the fitting function, A hmk 、B hmk 、C hmk Model parameters for power amplifier behaviorh m Fitting parameters of the function, k=1, …, K;fis the operating frequency of the power amplifier.
Compared with the prior art, the invention has the following beneficial effects:
the beneficial effects of the invention are as follows:
(1) The model parameters are set as functions of the working frequency, the functional relation between the model parameters and the working frequency is established, and compared with a traditional behavior model which can only simulate the low-frequency narrow-band envelope, the model has the capability of simulating the in-band fluctuation characteristic of the high-frequency broadband carrier wave, and the application scene of the model is greatly widened.
(2) The specific expression of the model equation is unlimited, and for any model equation, model parameters can be set as a function of frequency, so that the model equation has good universality, compatibility and portability, and can meet the requirements of engineering technicians on modifying the model equation according to actual needs without changing a parameter extraction method.
(3) The model parameter extraction method is convenient, easy to program and realize, high in modeling efficiency and easy to realize in commercial microwave/radio frequency circuit and system simulation software, so that the efficiency of radio frequency system simulation, design and optimization is improved.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a schematic diagram of the Hammerstein-Weiner model of the present invention.
FIG. 3 shows parameters a and h according to an embodiment of the present invention 0 Fitting effect as a function of frequency.
Fig. 4 shows the fitting effect of parameters c and d according to the frequency variation in the embodiment of the present invention.
FIG. 5 is a graph showing the comparison of the power scan simulation and the measurement results according to the embodiment of the invention.
FIG. 6 is a diagram showing the comparison between the simulation and measurement results of the frequency scanning according to the embodiment of the invention.
Detailed Description
As shown in fig. 1, the modeling method for a broadband nonlinear behavior model of a power amplifier including a memory effect provided in this embodiment includes the following steps:
step 1: establishing an initial power amplifier behavior model;
in this embodiment, an initial power amplifier behavior model is built by using a Hammerstein-Weiner model, as shown in fig. 2, where the Hammerstein-Weiner model includes a nonlinear function F (, a finite impulse response filter H (, a nonlinear function G (), and an input signal x (t) of the power amplifier sequentially passes through the nonlinear function F (), the finite impulse response filter H (, and the nonlinear function G (), to obtain an output signal y (t).
The memoryless nonlinear effect introduced by the active device in the power amplifier is characterized by 2 static nonlinear functions F (, and G (), and the dynamic memory effect introduced by the matching network, the bias network and the thermal effect is characterized by a finite impulse response FIR filter H (.
Setting a memory interval tau and a memory length M, and establishing an initial power amplifier behavior model according to a Hammerstein-Weiner model:
where y (t) is the output signal of the power amplifier, x (t) is the input signal of the power amplifier, h m Is a parameter of the finite impulse response filter, a, b, c, d is a parameter of the power amplifier behavior model,x(t-mτ) In a power amplifiert-m τInput signal of moment, τ is memoryM is the memory length, m=0, 1,2, …, M.
Step 2: extracting discrete values of each working frequency point of the initial power amplifier behavior model in the working frequency band;
firstly, determining each frequency point of a power amplifier behavior model in an operating frequency band;
in the method, in the process of the invention,for the ith operating frequency point of the power amplifier behavior model within the operating frequency band,as a starting point for the operating frequency band of the power amplifier behavior model,for the end of the operating frequency band of the power amplifier behavior model,the total number of points is uniformly sampled for the working frequency band, i=1, 2,3, …, nf.
Then, measuring the input-output characteristics of the power amplifier behavior model at each operating frequency point;
an input signal is applied to an input of an initial power amplifier operating model and an output signal amplitude of an output of the initial power amplifier operating model is measured.
The input signal of the embodiment is a frequency f i Is a single continuous wave |x j (t) | to characterize the nonlinear characteristics of the power amplifier, the amplitude of the single-tone continuous wave is scanned, and the amplitude of the single-tone continuous wave is set to be in the interval [ Mag ] min ，Mag max ]Internal scanning, the number of scanning points is N Mag The amplitude of the input signal isAt this time, the amplitude of the output signal is |y j (t)|，j=1，2，3，…，N Mag 。
Finally, constructing a nonlinear equation according to the input-output characteristics of the measured power amplifier behavior model at each working frequency point, and solving the discrete value of each working frequency point of the power amplifier behavior model in the working frequency band;
in I y j (t) | is the output signal amplitude of the power amplifier behavior model, |x j (t) | is the input signal of the power amplifier behavior model, j=1, 2,3, …, N Mag ，h m Is a real number parameter of the finite impulse response filter, a, b, c, d is a real number parameter, |x j (t-mτ) I is the behavior model of the power amplifier int-mτThe input signal at the moment τ is the memory interval, M is the memory length, m=0, 1,2, …, M.
The power amplifier behavior model includes a, b, c, d, h m M+5 unknown model parameters total, constructed m+5-element nonlinear equation set as follows:
when N is Mag >At M+5, solving the nonlinear equation set by combining a nonlinear overdetermined equation set numerical solution method (such as Newton-Lafson method, steepest descent method, conjugate gradient method, genetic algorithm and the like) to obtain an unknown number a, b, c, d, h m （m=0，1，2，…，M）。
Then the model parameters a, b, c, d, h can be solved for m (m=0, 1,2, …, M) each operating frequency point f within the modeled power amplifier operating frequency band i Is a discrete value of (a).
Step 3: fitting the relation of the working model of the power amplifier along with the change of frequency through a fitting function;
fitting the behavior model parameters a, b, c, d, h calculated in the step 2 by adopting a least square method m (m=0, 1,2, …, M) at each operating frequency point f i Is fit to the functional expression by the modeler according to model parameters a, b, c, d, h m (m=0, 1,2, …, M) with the operating frequency point f i Is properly selected; the fitting function can also be polynomial function, rational function, sine function, cosine function, spline function, etc., and the implementation routine is model parameter a, b, c, d, h m The fitting function of (m=0, 1,2, …, M) is as follows:
in the method, in the process of the invention,a、b、c、d、h m （m=0，1，2，…，M) A is a parameter of a power amplifier behavior model ak 、B ak 、C ak Model parameters for power amplifier behavioraParameters of the fitting function, A bk 、B bk 、C bk Model parameters for power amplifier behaviorbParameters of the fitting function, A ck 、B ck 、C ck Model parameters for power amplifier behaviorcParameters of the fitting function, A dk 、B dk 、C dk Model parameters for power amplifier behaviordParameters of the fitting function, A hmk 、B hmk 、C hmk Model parameters for power amplifier behaviorh m Fitting parameters of the function, k=1, …, K;fis the operating frequency of the power amplifier.
Step 4: parameters a, b, c, d, h of step 3 m The fitting function of (m=0, 1,2, …, M) is replaced into the initial power amplifier behavior model to obtain the final power amplifier behavior model.
The embodiment sets specific parameter values to obtain a final power amplifier behavior model.
The memory interval tau is 1/3 of the period of the input signal x (t), the memory length M=3, the starting point f of the working frequency band start =10 GHz, end of operating band f stop =70 GHz, total number of evenly sampled points N for working frequency band f =61, the amplitude of the single-tone continuous wave input signal is in the interval [ Mag min = -30dBm，Mag max = -5dBm]Internal scanning, the number of scanning points is N Mag =26，K=3。
The ith working frequency point f of the power amplifier in the working frequency band i I+9 ghz, i=1, 2,3, …,61, the amplitude of the input signal is Mag j = Mag min +(j-1) Mag step The fitting effect of the model parameters with frequency is shown in fig. 3 and 4, with j-31dbm, j=1, 2,3, …, 26.
The simulation result and measurement result pairs of the established power amplifier behavior model are shown in fig. 5 and 6. It can be seen that the power amplifier behavior model built according to the above embodiments can accurately characterize the broadband nonlinear characteristics of the power amplifier.
The foregoing is merely a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any modification and substitution based on the technical scheme and the inventive concept provided by the present invention should be covered in the scope of the present invention.
Claims (5)
1. A modeling method of a power amplifier broadband nonlinear behavior model containing a memory effect is characterized by comprising the following steps:
step 1: establishing an initial power amplifier behavior model;
step 2: acquiring a discrete value of each working frequency point of the initial power amplifier behavior model in a working frequency band;
firstly, determining each working frequency point of a power amplifier behavior model in a working frequency band;
，
in the method, in the process of the invention,for the ith operating frequency point of the power amplifier behavior model in the operating frequency band, +.>Starting point of operating frequency band for behavior model of power amplifier, < >>End point of operating frequency band for behavior model of power amplifier, < >>For the uniform sampling total number of the working frequency bands, i=1, 2,3, … and N f ；
Then, measuring the input-output characteristics of the power amplifier behavior model at each operating frequency point;
applying an input signal to an input of an initial power amplifier working model and measuring an output signal amplitude of an output of the initial power amplifier working model;
finally, constructing a nonlinear equation according to the input-output characteristics of the measured power amplifier behavior model at each working frequency point, and solving the nonlinear equation to obtain parameters a, b, c, d, h m (m=0, 1,2, …, M), obtaining a discrete value of each operating frequency point of the power amplifier behavior model in the operating frequency band;
step 3: fitting the parameters a, b, c, d, h obtained in step 2 by a fitting function m (m=0, 1,2, …, M) discrete values of each operating frequency point in the operating frequency band, looking at the relationship of the power amplifier behavior model with the frequency variation;
step 4: parameters a, b, c, d, h of step 3 m The fitting function of (m=0, 1,2, …, M) is replaced into the initial power amplifier behavior model to obtain the final power amplifier behavior model.
2. The modeling method for a broadband nonlinear behavior model of a power amplifier including a memory effect according to claim 1, wherein step 1 uses a Hammerstein-Weiner model to build an initial power amplifier behavior model:
，
where y (t) is the output signal of the power amplifier, a, b, c, d, h m As a parameter of the power amplifier behavior model,x(t-m τ) In a power amplifiert-mτThe input signal at the moment τ is the memory interval, M is the memory length, m=0, 1,2, …, M.
3. The modeling method of a power amplifier broadband nonlinear behavior model comprising memory effect according to claim 2, wherein constructing a nonlinear equation in step 2 is:
，
in I y j (t) is the output signal amplitude of the power amplifier behavior model, |x j (t-mτ) I is the behavior model of the power amplifier int-mτInput signal at time j=1, 2,3, …, N Mag ，N Mag For scanning point number in working frequency band of input signal a, b, c, d, h m τ is a memory interval, M is a memory length, m=0, 1,2, …, M, which is a parameter of the power amplifier behavior model.
4. The modeling method of wideband nonlinear behavior model of a power amplifier including memory effect as defined in claim 3, wherein said fitting function in step 3 can be one of polynomial function, rational function, sine function, cosine function, spline function.
5. The modeling method of power amplifier broadband nonlinear behavior model including memory effect according to claim 4, wherein the power amplifier behavior model parameters a, b, c, d, h m The fitting function of (m=0, 1,2, …, M) is as follows:
，
in the method, in the process of the invention,a、b、c、d、h m （m=0，1，2，…，M) A is a parameter of a power amplifier behavior model ak 、B ak 、C ak Model parameters for power amplifier behavioraParameters of the fitting function, A bk 、B bk 、C bk Model parameters for power amplifier behaviorbParameters of the fitting function, A ck 、B ck 、C ck Model parameters for power amplifier behaviorcParameters of the fitting function, A dk 、B dk 、C dk Model parameters for power amplifier behaviordParameters of the fitting function, A hmk 、B hmk 、C hmk Model parameters for power amplifier behaviorh m Fitting parameters of the function, k=1, …, K;fis the operating frequency of the power amplifier.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410275153.0A CN117875246B (en) | 2024-03-12 | 2024-03-12 | Modeling method for power amplifier broadband nonlinear behavior model containing memory effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410275153.0A CN117875246B (en) | 2024-03-12 | 2024-03-12 | Modeling method for power amplifier broadband nonlinear behavior model containing memory effect |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117875246A true CN117875246A (en) | 2024-04-12 |
| CN117875246B CN117875246B (en) | 2024-05-14 |
Family
ID=90595147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410275153.0A Active CN117875246B (en) | 2024-03-12 | 2024-03-12 | Modeling method for power amplifier broadband nonlinear behavior model containing memory effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117875246B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070063769A1 (en) * | 2003-05-07 | 2007-03-22 | Siemens Aktiengesellschaft | Arrangement and method for digital predistortion of a complex baseband input signal |
| KR20090125597A (en) * | 2008-06-02 | 2009-12-07 | 주식회사 케이티 | Method for extracting nonlinear model parameter of wideband signal using narrowband signal, apparatus and method for digital predistortering its using |
| US20150043678A1 (en) * | 2013-05-09 | 2015-02-12 | King Fahd University Of Petroleum And Minerals | Scalable digital predistortion system |
| US20150194989A1 (en) * | 2013-06-05 | 2015-07-09 | Telefonaktiebolaget L M Ericsson (Publ) | Digital predistortion of wideband power amplifiers with reduced observation bandwidth |
| CN106877825A (en) * | 2017-01-25 | 2017-06-20 | 东南大学 | The digital predistortion apparatus and method of the simplified nonlinear filter based on band limit |
| CN107359864A (en) * | 2017-07-25 | 2017-11-17 | 中国工程物理研究院电子工程研究所 | The adaptive agile digital pre-distortion method of frequency agility power amplifier |
| CN108199690A (en) * | 2018-01-05 | 2018-06-22 | 东南大学 | The power amplifier digital predistortion apparatus and method of band limit DDR function models based on cubic spline |
| US20220200540A1 (en) * | 2020-12-21 | 2022-06-23 | Ulak Haberlesme A.S. | Model trainer for digital pre-distorter of power amplifiers |
| CN116319202A (en) * | 2023-02-08 | 2023-06-23 | 东南大学 | Basis function optimization selection method suitable for radio frequency power amplifier model |
-
2024
- 2024-03-12 CN CN202410275153.0A patent/CN117875246B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070063769A1 (en) * | 2003-05-07 | 2007-03-22 | Siemens Aktiengesellschaft | Arrangement and method for digital predistortion of a complex baseband input signal |
| KR20090125597A (en) * | 2008-06-02 | 2009-12-07 | 주식회사 케이티 | Method for extracting nonlinear model parameter of wideband signal using narrowband signal, apparatus and method for digital predistortering its using |
| US20150043678A1 (en) * | 2013-05-09 | 2015-02-12 | King Fahd University Of Petroleum And Minerals | Scalable digital predistortion system |
| US20150194989A1 (en) * | 2013-06-05 | 2015-07-09 | Telefonaktiebolaget L M Ericsson (Publ) | Digital predistortion of wideband power amplifiers with reduced observation bandwidth |
| CN106877825A (en) * | 2017-01-25 | 2017-06-20 | 东南大学 | The digital predistortion apparatus and method of the simplified nonlinear filter based on band limit |
| CN107359864A (en) * | 2017-07-25 | 2017-11-17 | 中国工程物理研究院电子工程研究所 | The adaptive agile digital pre-distortion method of frequency agility power amplifier |
| CN108199690A (en) * | 2018-01-05 | 2018-06-22 | 东南大学 | The power amplifier digital predistortion apparatus and method of band limit DDR function models based on cubic spline |
| US20220200540A1 (en) * | 2020-12-21 | 2022-06-23 | Ulak Haberlesme A.S. | Model trainer for digital pre-distorter of power amplifiers |
| CN116319202A (en) * | 2023-02-08 | 2023-06-23 | 东南大学 | Basis function optimization selection method suitable for radio frequency power amplifier model |
Non-Patent Citations (1)
| Title |
|---|
| 李明玉;何松柏;李晓东;: "基于测量的记忆多项式功率放大器特征模型", 电子测量与仪器学报, no. 08, 15 August 2009 (2009-08-15), pages 53 - 59 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117875246B (en) | 2024-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106877825B (en) | Digital predistortion device and method based on band-limited simplified nonlinear filter | |
| Liu et al. | Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks | |
| CN115630617B (en) | S2P data conversion S2D model and ADS simulation file construction method | |
| CN115642971B (en) | Simulation file generation method and automatic extraction system for nonlinear parameters of microwave module | |
| Xu et al. | Generalized two-box cascaded nonlinear behavioral model for radio frequency power amplifiers with strong memory effects | |
| JP5323950B2 (en) | Method and apparatus for linearization of nonlinear power amplifier | |
| CN108199690A (en) | The power amplifier digital predistortion apparatus and method of band limit DDR function models based on cubic spline | |
| CN117875246B (en) | Modeling method for power amplifier broadband nonlinear behavior model containing memory effect | |
| CN113221308A (en) | Transfer learning rapid low-complexity modeling method facing power amplifier | |
| CN116502508A (en) | Real-time simulation method, system and related equipment for electric thermal performance of elastic wave filter | |
| Zhou et al. | A nonlinear memory power amplifier behavior modeling and identification based on memory polynomial model in soft-defined shortwave transmitter | |
| CN114358046B (en) | Multi-complexity-level complex electromagnetic interference environment simulation generation method and system | |
| Liu et al. | RF power amplifier modeling and linearization with augmented RBF neural networks | |
| Yuan et al. | Behavioral modeling of RF power amplifiers with memory effects using orthonormal hermite polynomial basis neural network | |
| Gong et al. | A Memorized Recurrent Neural Network Design for Wide Bandwidth PA Linearization | |
| CN110348047B (en) | Terahertz power amplifier circuit design method based on field-circuit cooperation | |
| CN113852586A (en) | Signal processing method and device based on 5G communication | |
| CN113468814A (en) | Digital predistortion training data screening method and device based on neural network | |
| CN102855417A (en) | Memory nonlinear model of wide-band radio frequency power amplifier and modeling method | |
| Giannini et al. | Neural networks and volterra series for time‐domain power amplifier behavioral models | |
| WO2024138908A1 (en) | Test excitation signal strain generation apparatus based on autonomous matching of functional link resources | |
| Stegmayer et al. | Volterra NN-based behavioral model for new wireless communications devices | |
| CN110276100B (en) | Behavior model modeling and implementation method based on Volterra series | |
| Cunha et al. | General nonlinear feed-forward RF model for power amplifiers | |
| Stegmayer | Neural networks and volterra series for modeling new wireless communication devices |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |