CN113472459B - A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links - Google Patents
A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links Download PDFInfo
- Publication number
- CN113472459B CN113472459B CN202110584326.3A CN202110584326A CN113472459B CN 113472459 B CN113472459 B CN 113472459B CN 202110584326 A CN202110584326 A CN 202110584326A CN 113472459 B CN113472459 B CN 113472459B
- Authority
- CN
- China
- Prior art keywords
- receiver
- link
- radio frequency
- output
- nonlinear effect
- 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.)
- Active
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 90
- 230000009022 nonlinear effect Effects 0.000 title claims abstract description 78
- 238000006243 chemical reaction Methods 0.000 claims description 23
- 238000004458 analytical method Methods 0.000 claims description 20
- 230000004044 response Effects 0.000 claims description 7
- 230000006835 compression Effects 0.000 claims description 6
- 238000007906 compression Methods 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000000034 method Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 11
- 230000008859 change Effects 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 238000005259 measurement Methods 0.000 description 8
- 230000006399 behavior Effects 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 5
- 230000009021 linear effect Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000003062 neural network model Methods 0.000 description 3
- 230000010355 oscillation Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000003542 behavioural effect Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/20—Monitoring; Testing of receivers
- H04B17/29—Performance testing
Landscapes
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Monitoring And Testing Of Transmission In General (AREA)
Abstract
The invention provides a comprehensive test and modeling platform for nonlinear effect of a radio frequency link of a receiver, which comprises the following steps: the signal source, the band-pass filter, the receiver link, the test board card and the upper computer are connected in sequence, signals processed by the band-pass filter are input to the receiver link, the receiver link is output to the test board card, and the signals processed by the test board card are output to the upper computer; the test board outputs according to the relation between the input power and the output power, the test board comprises: model coefficients under the current receiver link nonlinear effect model type, an automatic double-frequency graph of a selectable bandwidth range, the receiver link nonlinear effect model type after data fitting, an input and output power real-time corresponding graph and receiver related parameters under different frequency points are displayed by an upper computer.
Description
Technical Field
The invention belongs to the field of receiver radio frequency link testing, and particularly relates to a comprehensive testing and modeling platform for a receiver radio frequency link nonlinear effect.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Because the radio frequency link of the receiver is formed by cascading a plurality of active devices and passive devices, the devices have nonlinear distortion, and each stage of module of a signal in the radio frequency link experiences nonlinear distortion to a certain degree, the nonlinear effect of the radio frequency link of the receiver is more complex than that of a single device. The design of the radio frequency link of the receiver is to adopt a certain receiver type to realize certain performance parameters (parameters such as gain, noise and the like) according to application requirements, so that the selection of devices is different from the number, the type, the performance and the like, and each radio frequency link of the receiver has respective uniqueness.
At present, the testing and modeling of the nonlinear effect mainly focuses on the testing and modeling on the device level, and particularly takes the nonlinear effect modeling of the low noise amplifier as a representative. In terms of testing receiver nonlinear effects, there are currently several ways to test receiver radio frequency link nonlinear effects:
(1) the magnitude of the interference signal level is recorded in dBuV or dBm. Firstly, applying a sensitivity signal level at the input end of a receiver to obtain an output signal level; then two strong interference signal levels are applied to the receiver, the interference signal levels are adjusted so that the intermodulation products generated are equal in magnitude to the previous output signal levels, and the nonlinearity of the receiver is characterized by the strength of the interference signal levels at that time. The method cannot test the nonlinear effect when small signals are excited.
(2) The method of intermodulation factors is as above except that the difference between the level of the strong interfering signal and the level of the sensitive signal is recorded in dB. For example, the intermodulation factor of a receiver is 80dB, which means that the interference signal level is at least 80dB greater than the sensitivity signal level, producing the same output signal level. This definition method is applicable to strong interference situations.
(3) Two and three order intermodulation intercept values are adopted, and the unit is dBm. The second and third order intermodulation intercept point values are a hypothetical signal level value, but are more convenient in engineering application. Firstly, it is independent of the magnitude of the input signal level and can be easily used to compare the merits of the nonlinear performance of different receiver radio frequency links, secondly, it has a definite relationship with the Spurious Free Dynamic Range (SFDR) of the receiver, and thirdly, the second and third order intermodulation intercept values can be used to estimate the relationship between the useful signal level and the level of the intermodulation products, for example, when the receiver inputs the third order intermodulation intercept value of 20dBm, in the case of-lOdBm input signal, the third order intermodulation products have a magnitude of-70 dBm. Finally, the concept of a cut-off value is well suited for the analysis of the performance of the cascade of circuits inside the receiver.
The third-order intercept point is an important index for measuring the linearity of the receiver, but the third-order intercept point of the system cannot be directly measured. During actual measurement, the receiving system saturates the constant amplitude double tone signal generated by the power synthesizer before the input signal reaches the third-order intercept point, the constant amplitude double tone signal is added to the input end of the receiver, the constant amplitude double tone signal is adjusted until the output end generates the third-order intermodulation component, the relative inhibition degree of the fundamental component to the three-order intermodulation quantity is measured, and then the input third-order intercept point can be estimated.
(4) The most advanced international non-linear testing Technique is currently the dual-Frequency automatic testing Technique (ADFTT) proposed by the electromagnetic compatibility research and development laboratory of the university of radio technology and national information and russia. The two signal sources are used for respectively carrying out fast scanning and slow scanning in the test frequency band, so that frequency combinations covering all double-frequency signals in the test frequency band can be generated, a tested receiving system is tested, the used excitation signals are not omitted in the frequency combinations, and all nonlinear responses of a tested receiver can be excited theoretically.
With the development of Nonlinear circuit theory, some behavioral level methods for analyzing a receiver radio frequency link are proposed in succession, including time domain Analysis, widely used harmonic balance method, Volterra series Analysis method suitable for weak Nonlinear circuit Analysis, and Discrete Nonlinear Analysis (DNA) method based on polynomial approximation technique, etc.
The nonlinear effect model can be roughly divided into a physical model at a circuit level and a behavioral model at a system level. The nonlinear characterization of the components in the circuit model requires the establishment of a small-signal equivalent model and a large-signal analysis model under direct-current bias, the simulation result is accurate, but the calculation is time-consuming. The behavior model, also called black box model, is mainly used for describing the behavior characteristics of the nonlinear circuit or system, and because the internal physical structure of the circuit or system does not need to be considered and only depends on the input and output data of the circuit or system, the behavior model can be used for carrying out analysis on the system under the condition of lacking an actual circuit schematic diagram, and has higher computational efficiency.
Nonlinear systems can be classified into memoryless systems and memoryless systems. A memoryless system means that the output signal is a transient response of the input signal, independent of past conditions. With memory systems, the output is related to not only the current input, but also the past state.
The analysis of the nonlinear system is far more complex than the analysis of the linear system, and various nonlinear models in different forms can be established according to different requirements. According to the nature of the system, the model can be divided into a non-memory model and a memory model. The modeling mode can be divided into two types, namely behavior modeling based on system behaviors and physical models based on circuit device simulation. The behavior model is to regard the system as a black box, and describe the system through the input and output characteristics of the system. Physical models analyze the characteristics of a system by building the internal structure of the system. For each of the above models, parameters of the system need to be determined to describe a nonlinear system. There are two main methods for parameter determination, analytical method and experimental method. The analytic method is to determine specific parameters according to the physical principle of the system and given conditions. The experimental method is to obtain actual data through measurement and determine system parameters through interpolation, tabulation or curve fitting.
A large number of models already exist for modeling a nonlinear system, the models generally adopted for modeling the memoryless nonlinear system include a power series model, a display look-up table (LUT) model, a Saleh model, a memoryless polynomial model, a complex single-input single-output feedforward neural network model, a double-input double-output real number neural network model, a memoryless neural network model and the like, and the memoryless nonlinear model cannot accurately reflect the dynamic nonlinearity of a radio frequency link of a receiver.
The memory nonlinear system can be generally expressed by a functional series expansion, and the model comprises a memory polynomial model, a Volterra series model, a generalized memory polynomial model, a Hammerstein model, a Wiener model, an improved Hammerstein model, an improved Wiener model, a multi-type combination model, a neural network structure with different time delay topological structures based on a BP algorithm and the like.
The research cross of the nonlinear effect is strong, the related scope is wide, and a standard and unified research method is not formed yet. At the present stage, there are two main research technical routes: one is from the experiment, obtain a large amount of nonlinear effect data through the experimental actual measurement, carry out data statistics analysis, summarize some macroscopic nonlinear effect laws. Meanwhile, a corresponding database is established and a statistical model is established under data analysis. And secondly, starting from a nonlinear effect model, establishing a basic model, performing theoretical analysis and simulation by using the model, and performing model verification through experiments to obtain an effect rule. However, the former is based on experiments, only mathematical statistics can be carried out from the phenomenon level, and powerful analysis and understanding of effect mechanisms are lacked; in the latter case, the modeling reliability of the basic model is low and the knowledge of regularity can only be obtained.
Therefore, the current comprehensive test and modeling platform for the nonlinear effect of the radio frequency link of the receiver is still in a blank state, the test platform is temporarily set up during nonlinear research, and a primary test result is applied to other aspects of research and application of the radio frequency link of the receiver.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a comprehensive test and modeling platform for the nonlinear effect of a radio frequency link of a receiver, which comprehensively considers experimental measured data and a nonlinear effect model and combines the experimental measured data and the nonlinear effect model. The method can select a corresponding nonlinear effect model, supports signal source frequency sweep under a wide frequency band to carry out actual measurement, calculates model coefficients after selecting the existing model, and simultaneously carries out comprehensive data analysis and fitting of a stable model by means of machine learning.
The invention provides a comprehensive test and modeling platform for a receiver radio frequency link nonlinear effect.
A comprehensive test and modeling platform for receiver radio frequency link nonlinear effects comprises: the system comprises a signal source, a band-pass filter, a receiver radio frequency link, a test board card and an upper computer which are connected in sequence, wherein signals processed by the band-pass filter are input into the receiver radio frequency link, the receiver radio frequency link is output to the test board card, and the signals processed by the test board card are output to the upper computer; the test board outputs according to the relation between the input power and the output power, the test board comprises: model coefficients under the current receiver radio frequency link nonlinear effect model type, an automatic double-frequency graph of a selectable bandwidth range, the receiver radio frequency link nonlinear effect model type after data fitting, a model coefficient real-time corresponding graph of input and output power under different frequency points and relevant parameters of the receiver to an upper computer, and the relevant parameters are displayed by the upper computer.
Further, the signal source is processed by a filter and then input into the combiner, and the obtained single-path signal is input into a radio frequency link of the receiver.
Further, the single-channel signal is input into a receiver radio frequency link to obtain the relation between input and output responses of different frequency points, the type of the current receiver radio frequency link nonlinear effect model is selected according to the relation between the input and output responses of different frequency points, and the current model coefficient, the type of the receiver radio frequency link nonlinear effect model after data fitting and the model coefficient are calculated.
Further, when one signal source is input, the upper computer outputs a relation graph of the output power of the radio frequency link of the receiver and the change gain along with the power change of the input signal when the output of the upper computer is not interfered, and related parameters of the radio frequency link of the receiver in a corresponding frequency band.
Further, when two paths of signals are input, the upper computer outputs a relation graph of the output power and the conversion gain of the radio frequency link of the receiver under different interference signals (an intermodulation signal and a path of interference signal) along with the change of the input signal power, a relation graph of the output signal power and the conversion gain along with the change of the interference power, a relation graph of the signal output and the interference output along with the change of the interference power, and related parameters of the radio frequency link of the receiver in a corresponding frequency band.
Further, after the test board outputs the intermediate frequency signal, the power is divided into two paths for output, one path outputs the nonlinear effect model type and the current model coefficient of the radio frequency link of the first current receiver, the other path outputs the signal to the test board for processing after passing through the time delay, the nonlinear effect model type and the current model coefficient of the radio frequency link of the second current receiver are output, then the nonlinear effect model type and the current model coefficient of the radio frequency link with the first current receiver, the nonlinear effect model type and the current model coefficient of the radio frequency link of the second current receiver and the current model coefficient are jointly judged, and the nonlinear effect model type and the current model coefficient of the radio frequency link of the current receiver, the nonlinear effect model type and the model coefficient of the radio frequency link of the receiver after data fitting are output.
Further, the test board card outputs two paths of signals, one path of signal is input into an upper computer to obtain the nonlinear effect model type of the radio frequency link of the current receiver, and the current model coefficient is calculated according to the model type; and the other path outputs short-time data, stores the data, performs fitting analysis on the stored data and the data stored in a period of time according to the model type, performs data fitting by using a machine learning related algorithm, and outputs a nonlinear coefficient of the receiver in a period of time to an upper computer after fitting analysis.
Further, the radio frequency link related parameters of the receiver in the corresponding frequency band include: 1dB compression point, cut-point value, noise figure.
Further, the receiver radio frequency link nonlinear effect model types include: a direct gain receiver, a direct conversion receiver, a single conversion superheterodyne receiver, a double conversion superheterodyne receiver.
Further, the receiver rf link outputs:
the output response of the dual-frequency test signal input to the radio frequency link of the receiver is as follows:
the dual-frequency graph point coordinates corresponding to the output power of the receiver radio frequency link under different specified threshold values are as follows:
Wi(f1,f2|Uti)＝sgn{H(f1,f2)-Uti}
in the formula: u shapeoutIs the output signal level; u shape1in，U2inAre respectively a signal f1,f2The amplitude level of (d); u shapetiI is 1,2,3 …, which is a specified threshold value; sgn () is a sign function.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the nonlinear effect existing in a radio frequency link of a receiver, a universal comprehensive test and modeling platform is provided, which can be used for measuring and modeling the nonlinear effect of the radio frequency links of various types of receivers in a plurality of frequency bands between 150MHz and 40GHz, displaying the nonlinear effect parameters and models of the radio frequency links of the receivers in real time, providing long-time nonlinear effect measurement data and nonlinear effect stable models, and specially solving the problem of nonlinear effect test and modeling aiming at different frequency bands, different types of receivers and single devices (such as amplifiers and mixers).
The problem can be solved in a targeted manner, the universality is high (serving as a universal test platform), resources are saved, the cost is low, and after later-stage optimization, the device can be produced into a nonlinear effect test instrument in a mass production manner, so that the blank of the current instrument is filled.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a block diagram of a comprehensive testing and modeling platform for the nonlinear effect of the radio frequency link of the receiver of the present invention;
FIG. 2 is a receiver radio frequency link (device) performance test subsystem;
FIG. 3 is a block diagram of a multi-branch auto-fit test subsystem architecture;
FIG. 4 is a block diagram of a non-linear effect testing subsystem architecture based on machine learning.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
A comprehensive test and modeling platform for receiver radio frequency link nonlinear effects comprises: the system comprises a signal source, a band-pass filter, a receiver radio frequency link, a test board card and an upper computer which are connected in sequence, wherein signals processed by the band-pass filter are input into the receiver radio frequency link, the receiver radio frequency link is output to the test board card, and the signals processed by the test board card are output to the upper computer; the test board outputs according to the relation between the input power and the output power, the test board comprises: model coefficients under the current receiver radio frequency link nonlinear effect model type, an automatic double-frequency graph of a selectable bandwidth range, the receiver radio frequency link nonlinear effect model type after data fitting, a model coefficient real-time corresponding graph of input and output power under different frequency points and relevant parameters of the receiver to an upper computer, and the relevant parameters are displayed by the upper computer.
The invention provides a universal test platform for the nonlinear effect of a receiver (microwave device) for the first time, and the universal test platform is capable of integrating functions of a two-dimensional/three-dimensional double-frequency graph, a model type, a real-time model coefficient, an input and output power graph under different frequency points, nonlinear effect related parameters (1dB compression point and the like), a receiver model coefficient under long-time test (a stability coefficient obtained by long-time test and data fitting after different models are selected), and the like, and selecting different model types and calculating corresponding model coefficients.
As shown in fig. 1, the most important point of the present invention is to realize a universal integrated test and modeling platform, which can be used for testing and modeling the nonlinear effect of radio frequency links of various types of receivers in multiple frequency bands between 150MHz and 40GHz, and can display functions such as a two-dimensional/three-dimensional dual-frequency diagram, a model type, a real-time model coefficient, an input/output power diagram at different frequency points, a nonlinear effect related parameter (such as 1dB compression point) and a receiver model coefficient (a stability coefficient obtained by long-time test and data fitting after different models are selected) under a long-time test on an upper computer interface.
Generally, a dual-tone signal is used for testing a nonlinear effect, and in reality, an electromagnetic environment is complex, and an interference signal is not in a path, so that a three-path signal source (capable of realizing frequency sweeping within a certain bandwidth) is used for inputting to simulate a real electromagnetic environment. A user can select a single-path signal source, a two-path signal source or a three-path signal source to input according to the complexity of a radio frequency link (or a device) of a test receiver, an application scene electromagnetic environment and other factors.
After the signal output by the signal source is processed by the filter, two paths or three paths are combined into one path of signal by the combiner and input into a radio frequency link (or device) of the receiver, the radio frequency link (or device) of the receiver is simultaneously output to the test board card and the frequency spectrograph, and the signal is output to the upper computer after being processed by the test board card. The single-channel signal source is input into the receiver radio frequency link, and the relation between the input power and the output power of different frequency points can be obtained, so that the receiver radio frequency link parameters such as the actual measurement link gain of different frequency points in the actual measurement can be obtained.
The chart data of the dual-frequency test subsystem is given in f1,f2The straight line representation in the rectangular coordinate system with coordinate axes has the following forms for different types of receivers:
1) direct gain receiver
k1f1+k2f2＝f0；
k1,k2＝0,±1,±2,…；min{|k1|+|k2|}＝1；L＝|k1|+|k2|；
2) Direct conversion (zero intermediate frequency) receiver
k1f1+k2f2-kgfg＝0；
k1,k2＝0,±1,±2,…；kg＝0,1,2,…；fg＝f0；
min{|k1|+|k2|}＝1；L＝|k1|+|k2|；
3) Single conversion superheterodyne receiver
k1f1+k2f2＝kgfg+kintfint；
k1,k2＝0,±1,±2,…；kg＝0,1,2,…；kint＝±1；
min{|k1|+|k2|}＝1；L＝|k1|+|k2|；
4) Double-conversion superheterodyne receiver
k1f1+k2f2＝kg1fg1+kg2fg2+kintfint；
k1,k2＝0,±1,±2,…；kg1,kg2＝0,1,2,…；kint＝±1；
min{|k1|+|k2|}＝1；L＝|k1|+|k2|；
In these equations, k1,k2The harmonic times of the two test signals; k is a radical ofg1,kg2The harmonic times of two local oscillation signals of the superheterodyne receiver are obtained; f. ofg1,fg2The frequency of a local oscillation signal of the superheterodyne receiver; k is a radical ofintIndicating the type of frequency conversion (up-conversion or down-conversion); f. ofintA frequency that is an intermediate frequency; f. of0Adjusting the frequency for the direct gain receiver and the direct conversion receiver; k is a radical ofgThe harmonic frequency of the local oscillation signal of the direct frequency conversion receiver is obtained; f. ofgFor the first harmonic frequency (f) of the local oscillator signal of a direct conversion receiverg＝f0) (ii) a L is the order of the measured receiver frequency channel represented in the dual frequency plot data: l-1 denotes a linear channel, L>1 denotes a non-linear channel.
The receiver output characteristics are based on:
in the formula: u shapeoutIs the output signal level; u shape1in，U2inAre respectively a signal f1,f2Of (c) is detected.
Wi(f1,f2|Uti)＝sgn{H(f1,f2)-Uti}
In the formula: u shapetiI is 1,2,3 …, which is a specified threshold value; sgn () is a sign function.
Different double-frequency images can be obtained according to different threshold values, and the method is a signal processing technology of the automatic double-frequency test.
As shown in fig. 2, in the test subsystem, one signal source (rf signal) or two signal sources (rf signal and interference signal) can be selected.
When one signal source (radio frequency signal) is selected to be input, the correlation performance of a radio frequency link of a receiver in any frequency band can be tested under the condition of no interference signals. The input signal is output to the spectrometer and the test board card simultaneously after passing through the radio frequency link (device) of the receiver, the test board card processes the signal and outputs the processed signal to the upper computer, and the output power and conversion gain of the radio frequency link of the receiver are changed along with the power of the input signal when the interface of the upper computer displays the interference-free relation diagram, and the related parameters (1dB compression point, cut point value, noise coefficient and the like) of the radio frequency link of the receiver in the corresponding frequency band.
When two paths of signal sources (radio frequency signals and interference signals) are selected to be input, the correlation performance of a radio frequency link of a receiver in any frequency band can be tested under different interference signals. The radio frequency signal and the interference signal are input to a radio frequency link of the receiver through a combiner; the signals are simultaneously output to a frequency spectrograph and a test board card after passing through a radio frequency link (device) of a receiver; the output signal is connected with a frequency spectrograph and used for monitoring the signal change condition; and outputting the output power and conversion gain of the radio frequency link of the receiver along with the change of the input signal power, the output signal power and conversion gain along with the change of the interference power, the signal output and interference output along with the change of the interference power, and related parameters (1dB compression point, cut-off value, noise coefficient and the like) of the radio frequency link of the receiver in a corresponding frequency band to an upper computer through displaying on an interface of the upper computer.
As shown in fig. 3, two same-amplitude different-frequency signal sources in the multi-branch automatic fitting test subsystem are the same as those in the previous schemes, and are sequentially subjected to filtering processing by a filter, and are input to the input end of a receiver by a combiner (the influence of the combiner on the nonlinear effect of a two-tone signal is not considered), the signal is processed by the receiver, and then an intermediate frequency signal is output to two branches by a power divider, one branch is processed by a measuring instrument, and then a current receiver nonlinear coefficient is output, the other branch is processed by a time delayer, and a current receiver nonlinear coefficient (multi-branch and different-delay processing can be performed) is output, then the two branches are subjected to joint decision, so that performance parameter data of the same signal processed by the receiver under the same condition is ensured, the nonlinear coefficient of the receiver for a period of time is output by joint decision fitting (the longer the storage time is, the data volume is larger, the more accurate the nonlinear coefficient is)
Because the test environments at each time point may have differences, for example, environmental factors, external interference sources, etc. may cause measurement errors of the same signal, it is difficult to ensure that the non-linear characteristics of the receiver are completely measured in the same test environment. Therefore, the nonlinear characteristic of the receiver is completely measured under the same test environment by adopting the multi-branch automatic fitting test subsystem under the condition of delaying N times as much as possible.
As shown in fig. 4, two (three) signal sources with the same amplitude and different frequencies output are filtered by a band-pass filter, harmonics and clutter are removed as much as possible, two signals are input to the input end of a receiver through a combiner (the nonlinear effect influence of the combiner on a two-tone signal is not considered), the signals are processed by the receiver and then output to a frequency spectrograph and a measuring instrument simultaneously, the signals are mutually modulated while the receiver processes the signals to form intermodulation signals, and a radio frequency link of the receiver has broadband performance, can receive various signals in a frequency band and is a main component for generating intermodulation, so that the frequency spectrograph is used for directly utilizing intermediate frequency to perform intermodulation signal testing during analysis, and various combined frequency components can be measured. Meanwhile, the measuring instrument also receives the same intermediate frequency signal, the test function is expanded, two paths of output signals of the test system are tested, one path of signal enables the measuring instrument to display the model type of the current receiver in real time while displaying the intensity of the intermodulation signal, and the current model coefficient is calculated according to the model type; and the other path outputs short-time data, stores the data, performs fitting analysis on the stored data and the data stored in a previous period of time according to the model type, performs data fitting by using a machine learning related algorithm in the part, and outputs a nonlinear coefficient of the receiver in a period of time after the fitting analysis (the longer the storage time is, the larger the data amount is, the more accurate the nonlinear coefficient is).
The nonlinear effect testing subsystem based on machine learning displays on the upper computer: the receiver radio frequency link nonlinear effect model type after data fitting, the current model coefficient, and the receiver radio frequency link nonlinear effect model type and model coefficient.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110584326.3A CN113472459B (en) | 2021-05-27 | 2021-05-27 | A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110584326.3A CN113472459B (en) | 2021-05-27 | 2021-05-27 | A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113472459A CN113472459A (en) | 2021-10-01 |
| CN113472459B true CN113472459B (en) | 2022-04-01 |
Family
ID=77871373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110584326.3A Active CN113472459B (en) | 2021-05-27 | 2021-05-27 | A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113472459B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115242326B (en) * | 2022-07-08 | 2024-10-22 | 苏州新阳升科技股份有限公司 | A method for constructing an analytical model for nonlinear storage effects in the front-end of a wideband receiver |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104242834A (en) * | 2014-08-15 | 2014-12-24 | 中国舰船研究设计中心 | Nonlinear response modeling method of receiver preamplifier based on high-order polynomial fitting |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7561857B2 (en) * | 2006-08-30 | 2009-07-14 | Infineon Technologies Ag | Model network of a nonlinear circuitry |
| ATE510350T1 (en) * | 2008-06-18 | 2011-06-15 | Nokia Siemens Networks Spa | METHOD FOR TRANSMISSION FUNCTIONAL LINEARIZATION OF HIGH-POWER AMPLIFIERS USING ADAPTIVE CALCULATION OF THE MAIN INTERMODULATION PRODUCTS OF HIGH-FREQUENT HARMONIC RESOLUTION |
| US9722646B1 (en) * | 2014-12-31 | 2017-08-01 | Physical Optics Corporation | Integrative software radio frequency management system and method for compensation of nonlinear response in radio frequency devices |
| CN105242132B (en) * | 2015-09-11 | 2017-12-15 | 中国电子科技集团公司第四十一研究所 | A kind of non-linear vector network parameter test device of double frequency and method |
| CN110501591A (en) * | 2019-08-20 | 2019-11-26 | 中国人民解放军国防科技大学 | System and method for testing electromagnetic environment adaptability of radio receiver |
| CN111726855B (en) * | 2020-06-30 | 2021-07-13 | 华北电力大学 | A power control optimization method for communication systems |
-
2021
- 2021-05-27 CN CN202110584326.3A patent/CN113472459B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104242834A (en) * | 2014-08-15 | 2014-12-24 | 中国舰船研究设计中心 | Nonlinear response modeling method of receiver preamplifier based on high-order polynomial fitting |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113472459A (en) | 2021-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Verspecht | Large-signal network analysis | |
| CN113472459B (en) | A Comprehensive Testing and Modeling Platform for Nonlinear Effects of Receiver RF Links | |
| CN102628897B (en) | Based on N1dB compression point and N2Three-order intermodulation test method of dB compression point | |
| Lavrador et al. | Evaluation of signal-to-noise and distortion ratio degradation in nonlinear systems | |
| US10379162B1 (en) | System for performing modulation analysis without using a modulated signal | |
| CN101689836A (en) | Method and apparatus for linearizing a nonlinear power amplifier | |
| Verbeyst et al. | The Volterra input-output map of a high-frequency amplifier as a practical alternative to load-pull measurements | |
| CN109088675A (en) | A kind of the access calibration method and device of radio-frequency signal source | |
| CN105991092A (en) | Mixer simulation method and mixer simulation device | |
| Cai et al. | An improved quadratic poly-harmonic distortion behavioral model | |
| Pascual et al. | System simulation of a differential radiometer using standard RF-microwave simulators | |
| CN115589267B (en) | A circuit analysis and prediction method for the intermodulation power frequency characteristics of passive components | |
| CN115374627B (en) | A Correction Method for Electromagnetic Environment Simulation Based on PCHIP | |
| Wang et al. | A Volterra mapping-based S-parameter behavioral model for nonlinear RF and microwave circuits and systems | |
| Wood et al. | Nonlinear microwave/RF system design and simulation using Agilent ADS'system-data models' | |
| Saabe et al. | RF Power Amplifier Model Extraction for Accurate and Fast Load Pull Simulations with Wideband Signals | |
| CN117688884B (en) | A quantitative simulation modeling method for predicting the conducted immunity of integrated circuits | |
| CN119619808B (en) | A harmonic test link of a device under test, an active load traction system and a method | |
| Sun et al. | Novel and simplified passive intermodulation distortion measurement using vector network analyzer | |
| Martins et al. | Multi-sine response of third order nonlinear systems with memory based on two-tone measurements | |
| Vanbeylen et al. | Application of blind identification to nonlinear calibration | |
| CN119740534A (en) | Broadband power amplifier model and power amplifier modeling, analysis method and system | |
| Cooman et al. | Determining the dominant nonlinear contributions in a multistage op-amp in a feedback configuration | |
| Pichler et al. | Load error correction for high power load dependent X-parameter measurements | |
| Van Moer et al. | Best linear approximation: Revisited |
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 |