CN116318210A - Compensation method, compensator and system for nonlinear distortion of pulse field source - Google Patents
Compensation method, compensator and system for nonlinear distortion of pulse field source Download PDFInfo
- Publication number
- CN116318210A CN116318210A CN202211678223.4A CN202211678223A CN116318210A CN 116318210 A CN116318210 A CN 116318210A CN 202211678223 A CN202211678223 A CN 202211678223A CN 116318210 A CN116318210 A CN 116318210A
- Authority
- CN
- China
- Prior art keywords
- compensation
- signal
- nonlinear
- nonlinear distortion
- distortion
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 239000013598 vector Substances 0.000 claims abstract description 94
- 238000001228 spectrum Methods 0.000 claims description 38
- 239000011159 matrix material Substances 0.000 claims description 35
- 230000003595 spectral effect Effects 0.000 claims description 24
- 238000010586 diagram Methods 0.000 claims description 16
- 230000005855 radiation Effects 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 14
- 238000010276 construction Methods 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 8
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 7
- 230000009021 linear effect Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 238000000605 extraction Methods 0.000 description 5
- 230000003446 memory effect Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000002238 attenuated effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/06—Receivers
- H04B1/10—Means associated with receiver for limiting or suppressing noise or interference
- H04B1/12—Neutralising, balancing, or compensation arrangements
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Amplifiers (AREA)
Abstract
The invention belongs to the technical field of communication, and particularly discloses a compensation method, a compensator and a system for nonlinear distortion of a pulse field source. The compensation method comprises the following steps: constructing a Volterra series model for nonlinear distortion of an analog digital receiver, and loading a signal to be compensated received by the digital receiver into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries nonlinear distortion quantity, and constructing a compensation model for representing the nonlinear distortion quantity; the nonlinear distortion quantity in the distortion signal is eliminated by using a compensation model, and compensation output is obtained; based on the compensation output, a least square method is adopted to update the compensation kernel vector of the compensation model, so that the nonlinear distortion quantity in the distortion signal is eliminated in real time by using the compensation model updated by the compensation kernel vector. The invention solves the problem of nonlinear distortion of a pulse field source, and can not directly compensate the nonlinearity of a receiver under the condition of not adding an additional analog-to-digital converter ADC.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a compensation method, a compensator and a system for nonlinear distortion of a pulse field source.
Background
The high-power pulse field source works in a microwave frequency band to generate a high-amplitude pulse radiation field, usually adopts an electronic vacuum device such as a klystron, a gyrotron and the like, has poor linearity of output power, different distortion of output waveforms and is an important difficult problem to be solved in how to improve the linearity of the pulse field source. Due to the physical structure and dispersion characteristics of the electronic vacuum device, the output linearity of the electronic vacuum device is difficult to be improved from the pulse source, and the electronic vacuum device is often realized by adopting a mode of improving the linearity of receiving equipment such as a broadband receiver, a mixer and the like. However, the linearity of the digital receiving front-end will be affected by the deficiency of the index such as the third-order output intermodulation of the radio frequency receiving front-end components such as amplifiers and mixers in the wideband receiver. High-speed ADCs at digital receivers are generally considered ideal devices because of the weak nonlinearities compared to the PA at the transmitting end. However, in practice, weak nonlinearities caused by the ADC at the front end of the receiver still cause ringing and are difficult to identify and calibrate due to weak nonlinearities. This can make it difficult for the system to guarantee a large spurious free dynamic range SFDR with non-linear distortion of the received signal. In the case of nonlinear distortion of the receiver, the spurious-free dynamic range of the system is reduced, and the acquired weak signal is very easily swamped by the strong nonlinear distortion component or other clutter.
For wideband digital receivers with high performance requirements, weak nonlinearity of each analog and analog-digital hybrid device also reduces the possibility of dynamic range improvement, and affects the detection capability of weak signals under the coexistence of strong and weak signals. Therefore, it is a primary task to design a high dynamic range wideband digital receiver to increase the linearity of the devices as much as possible. The ideal linear effect is difficult to achieve by continuously optimizing the analog circuit under the restriction of the prior art. The nonlinear problem of the front end of the receiver is reduced by using a certain digital compensation technology, which is a more common scheme at the present stage. The linearization technology widely adopted in the related field at present is mainly a power back-off method, a feedback technology, a feedforward technology, a predistortion technology and the like, wherein the predistortion technology is widely applied to solve the problem of nonlinear distortion of a transmitting end. If the predistortion concept is applied to the receiving end, the nonlinearity represented by the RF radio frequency front end and the analog-to-digital converter ADC is regarded as a nonlinearity system similar to the PA, the compensation scheme is to construct a compensation system similar to the digital predistortion DPD backward inversion, and the linearization of the receiving end is achieved by guaranteeing the integral linearity.
However, the input signal into the nonlinear system is an analog signal, and an additional analog-to-digital converter ADC is required to convert the signal to a digital input signal of the receiver. Such a structure has three disadvantages, namely, one of the three disadvantages is that the cost is increased by adding the high-speed analog-to-digital converter ADC; secondly, the ADC is required to have extremely high sampling rate and conversion accuracy when the radio frequency signal is required to be directly converted into the baseband as a reference signal; thirdly, the added ADC still has nonlinear influence compensation effect. It can be seen that the prior art cannot directly compensate for the nonlinearity of the receiver without adding an additional ADC, and it is difficult to improve the linearity of the radiation signal of the pulse field source by improving the linearity of the receiving device without adding an additional ADC.
Disclosure of Invention
The invention aims to provide a compensation method, a compensator and a system for nonlinear distortion of a pulse field source, which are used for solving the problem that the nonlinear distortion of the pulse field source in the related art cannot be directly compensated under the condition that an additional analog-to-digital converter ADC is not added.
To achieve the above object, according to a first aspect of the present invention, there is provided a compensation method for nonlinear distortion of a pulsed field source, comprising:
A Volterra series model for non-linear distortion of an analog digital receiver is constructed by adopting the Volterra series as a functional series, and a signal to be compensated received by the digital receiver is loaded into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries a non-linear distortion quantity;
constructing a compensation model for representing the nonlinear distortion quantity according to the Volterra series model, wherein the compensation model comprises a nonlinear memory matrix and a compensation kernel vector, the nonlinear memory matrix is composed of memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vector is composed of kernel coefficients of each order of the Volterra series model;
the nonlinear distortion amount in the distortion signal is eliminated by using the compensation model, and compensation output is obtained;
based on the compensation output, updating the compensation kernel vector of the compensation model by adopting a least square method so as to eliminate the nonlinear distortion quantity in the distortion signal in real time by utilizing the compensation model updated by the compensation kernel vector.
Further, based on the compensation output, the step of updating the compensation kernel vector of the compensation model using a least squares method includes:
Extracting a nonlinear distortion component from the compensation output, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
determining nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
Further, based on the compensation output, the step of updating the compensation kernel vector of the compensation model using a least squares method further includes:
determining the power spectral density of the distorted signal, and determining the frequency band information of the nonlinear distortion amount according to the power spectral density;
constructing a multi-passband filter according to the frequency band information;
extracting the nonlinear distortion component from the compensation output by adopting the multi-pass filter, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
Determining nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
Further, the step of determining the power spectral density of the distorted signal and determining the frequency band information of the nonlinear distortion amount according to the power spectral density includes:
performing discrete Fourier transform on the distorted signal to obtain the power spectrum density, and drawing a power spectrum density diagram according to the power spectrum density;
and determining the power spectrum density below the power spectrum threshold as the power spectrum density of the nonlinear distortion amount in the first Nyquist frequency band in the power spectrum density diagram based on a preset power spectrum threshold.
Further, the step of performing iterative operation on the compensation kernel vector of the compensation model by using a least square method based on the nonlinear signal power includes:
Taking an operation target for minimizing the nonlinear signal power as a cost function of the least square method, and constructing an autocorrelation matrix according to the cost function;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on an inverse matrix of the autocorrelation matrix.
In a second aspect of the present invention, there is provided a compensator for nonlinear distortion of a digital receiver, comprising:
the nonlinear simulation module is used for constructing a Volterra series model for simulating nonlinear distortion of the digital receiver by adopting the Volterra series as a functional series, and loading a signal to be compensated received by the digital receiver into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries nonlinear distortion quantity;
the compensation model construction module is used for constructing a compensation model for representing the nonlinear distortion quantity according to the Volterra series model, the compensation model comprises a nonlinear memory matrix and a compensation kernel vector, the nonlinear memory matrix is composed of memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vector is composed of kernel coefficients of each order of the Volterra series model;
The compensation output acquisition module is used for eliminating the nonlinear distortion quantity in the distortion signal by using the compensation model to obtain compensation output;
and the real-time elimination module is used for updating the compensation kernel vector of the compensation model by adopting a least square method based on the compensation output so as to eliminate the nonlinear distortion quantity in the distortion signal in real time by utilizing the compensation model updated by the compensation kernel vector.
Further, the real-time cancellation module includes:
the extraction unit is used for extracting a nonlinear distortion component from the compensation output, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
a determining unit configured to determine nonlinear signal power of the nonlinear distortion amount from the extracted k nonlinear distortion components;
and the iterative operation unit is used for carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
Further, the real-time cancellation module further includes:
the frequency band determining unit is used for determining the power spectrum density of the distortion signal and determining the frequency band information of the nonlinear distortion quantity according to the power spectrum density;
a filter construction unit configured to construct a multi-passband filter according to the frequency band information;
the extraction unit is used for extracting the nonlinear distortion component from the compensation output by adopting the multi-pass filter, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
a determining unit configured to determine nonlinear signal power of the nonlinear distortion amount from the extracted k nonlinear distortion components;
and the iterative operation unit is used for carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
In a third aspect of the present invention, there is provided a signal compensation system comprising:
a digital receiver comprising a compensator for digital receiver nonlinear distortion;
a pulsed field source for generating a pulsed radiation signal;
the digital receiver receives the pulse radiation signal and loads the pulse radiation signal as a signal to be compensated into the compensator facing the nonlinear distortion of the digital receiver.
Further, the digital receiver further includes:
a radio frequency front end;
the input end of the analog-to-digital converter is electrically connected with the output end of the radio frequency front end, and the output end of the analog-to-digital converter is electrically connected with the compensator facing the nonlinear distortion of the digital receiver.
Compared with the prior art, the technical scheme provided by the application has the following technical effects:
the invention directly compensates the signal received by the digital receiver without additionally adding an analog-digital converter ADC to acquire original input data, can largely eliminate nonlinear distortion of the digital receiver without obtaining the original input signal, extracts nonlinear quantity by identifying frequency distribution of nonlinear components, constructs a cost function of nonlinear distortion quantity signal energy in the compensation signal and identifies and updates compensation model parameters by the cost function, and can better improve the spurious-free dynamic range SFDR performance of the signal compensation system without adding an additional ADC to acquire the actual input signal.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method for compensating nonlinear distortion of a pulsed field source according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the specific execution flow of step S14 in FIG. 1;
FIG. 3 is a schematic diagram of a compensator for nonlinear distortion of a pulsed field source according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a signal compensation system according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a nonlinear post-compensation structure constructed in accordance with one embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating steps of a post-compensation simulation experiment of a digital receiver according to an embodiment of the present invention;
FIG. 7 is a power spectrum diagram of a simulated binaural signal nonlinear distortion in accordance with an embodiment of the present invention;
FIG. 8 is a flowchart of an adaptive parameter update of a compensation model according to an embodiment of the present invention;
fig. 9 is a power spectrum of the output signal after the simulation compensation in fig. 6.
Reference numerals illustrate:
10. a digital receiver; 11. a compensator; 111. a nonlinear simulation module; 112. the compensation model building module; 113. a compensation output acquisition module; 114. a real-time elimination module; 141. a frequency band determining unit; 142. a filter construction unit; 143. an extraction unit; 144. a determination unit; 145. an iterative operation unit; 12. an analog-to-digital converter; 13. a radio frequency front end; 20. a pulsed field source.
Detailed Description
The advantages and features of the present invention will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings and detailed description. It should be noted that the drawings are in a very simplified form and are adapted to non-precise proportions, merely for the purpose of facilitating and clearly aiding in the description of embodiments of the invention.
It should be noted that, in order to clearly illustrate the present invention, various embodiments of the present invention are specifically illustrated by the present embodiments to further illustrate different implementations of the present invention, where the various embodiments are listed and not exhaustive. Furthermore, for simplicity of explanation, what has been mentioned in the previous embodiment is often omitted in the latter embodiment, and therefore, what has not been mentioned in the latter embodiment can be referred to the previous embodiment accordingly.
In a first embodiment of the present invention, a compensation method for nonlinear distortion of a pulsed field source is provided, as shown in fig. 1, the compensation method comprising the steps of:
step S11: and constructing a Volterra series model for nonlinear distortion of the analog digital receiver 10 by adopting the Volterra series as a functional series, and loading a signal to be compensated received by the digital receiver 10 into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries nonlinear distortion quantity.
Step S12: according to the Volterra series model, a compensation model for representing nonlinear distortion is constructed, the compensation model comprises a nonlinear memory matrix and compensation kernel vectors, the nonlinear memory matrix is composed of memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vectors are composed of kernel coefficients of each order of the Volterra series model.
Step S13: and eliminating nonlinear distortion in the distortion signal by using a compensation model to obtain a compensation output.
Step S14: based on the compensation output, a least square method is adopted to update the compensation kernel vector of the compensation model, so that the nonlinear distortion quantity in the distortion signal is eliminated in real time by using the compensation model updated by the compensation kernel vector.
In particular, since the memory effect of the wideband digital receiver 10 is particularly obvious, the Volterra series is used as a functional series, and all the systems which can be described by continuous functional can be theoretically simulated, and the wideband digital receiver 10 is a commonly used memory nonlinear model and has universality, the embodiment of the invention adopts a discrete Volterra series model to simulate nonlinear distortion of the digital receiver 10, and the expression of the Volterra series model is as follows:
where x (k) is the kth discrete input signal (i.e., the signal to be compensated), k is a positive integer, y (k) is the corresponding output of x (k) after entering the Volterra series model, D is the nonlinear order of the input, and D is the maximum of D. N (N) d Is the maximum memory depth of the d-th order Volterra kernel. h (r) 1 ，r 2 ，…，r d ) Is the kernel coefficient corresponding to the Volterra series of the d-th order. The total number of kernel coefficients of the Volterra series model is:
in practice, as the memory depth increases, the signal amplitude is attenuated, and the influence of the high-order nonlinear distortion of the attenuation post term on the system linearity is greatly reduced. For a receiver, the spurious-free dynamic range of the system is most affected by low-order nonlinear distortion. Therefore, the Volterra series model of the low-order short memory effect can be adopted to simulate the nonlinear distortion of the receiver, for example, the Volterra series model with the maximum memory depth of 2 nd and 3 rd order items is adopted.
The compensation model in step S12 should simulate the nonlinear distortion of the digital receiver 10 as much as possible, and therefore, the compensation model adopts a similar structure to the Volterra series model in step S11 to characterize the nonlinear memory portion of the distorted signal. The expression of the compensation model B (k) is:
B(k)＝A T (k)×w。
where T is the transpose operator of the matrix, defining a matrix A that characterizes the nonlinear memory portion T (k) (i.e., a non-linear memory matrix) is a G x N matrix, where a (k) has the expression:
A(k)＝[v(k-G+1)v(k-G+2)…v(k-1)v(k)]。
where v (k) is a memory nonlinear column vector constructed from y (k) and each stage does not contain a linear term, and w is a compensation kernel vector to be identified.
v(k)＝[y 2 (k)y(k)y(k-1)…y 2 (k-N d +1)y 3 (k)y 2 (k)y(k-1)…y D (k-N d +1)] T 。
w＝[h(0，0)h(0，1)…h(N 2 -1，N 2 -1)h(0，0，0)h(0，0，1)…h(N D -1，…，N D -1)] T Wherein h is the nuclear coefficient of each order obtained by expanding the Volterra series model expression.
In step S13, the nonlinear distortion amount in the distortion signal is eliminated by using the compensation model, and the obtained compensation output is: s (k) =y (k) -B (k), wherein, for facilitating subsequent computation, a (k) is taken from a memory portion of the same length as y (k) for subsequent computation, whereby the expression of y (k) can be expressed as:
y(k)＝[y(k-G+1)y(k-G+2)…y(k-1)y(k)] T since v (k) in a (k) is a memory nonlinear column vector constructed of y (k), y (k) in the y (k) expression is the amount of memory nonlinear processing by x (k).
As shown in fig. 2, in step S14, the step of updating the compensation kernel vector of the compensation model using the least square method based on the compensation output includes:
step S141: and determining the power spectral density of the distorted signal, and determining the frequency band information of the nonlinear distortion amount according to the power spectral density.
Step S142: a multi-pass filter is constructed from the band information.
Step S143: and extracting a nonlinear distortion component from the compensation output by adopting a multi-passband filter, wherein the nonlinear distortion component is a value of nonlinear distortion quantity in a distortion signal obtained by loading a k signal to be compensated into a Volterra series model.
Step S144: and determining the nonlinear signal power of the nonlinear distortion quantity according to the extracted k nonlinear distortion components.
Step S145: based on nonlinear signal power, carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
In step S141, the step of determining the power spectral density of the distorted signal and determining the frequency band information of the nonlinear distortion amount according to the power spectral density includes: performing discrete Fourier transform on the distortion signal to obtain power spectrum density, drawing a power spectrum density diagram according to the power spectrum density, and determining the power spectrum density below the power spectrum threshold as the power spectrum density of the nonlinear distortion amount in a first Nyquist frequency band in the power spectrum density diagram based on a preset power spectrum threshold. In particular, for the entire receive frequency band of the wideband digital receiver 10, the received strong signal is typically distributed sparsely, whereas the nonlinear distortion component of the digital receiver 10 system may be considered to be primarily due to the strong signal. N-point discrete Fourier transform is calculated on the distorted signal, the power spectrum density of the distorted signal is calculated, and the power spectrum density expression is as follows:
Then, the power spectrum density of the distorted signal is drawnThe diagram sets a proper power spectrum threshold P hold It can be considered that the power spectral density in the first nyquist band in the power spectral density diagram exceeds the power spectral threshold P hold Is a strong signal to be received. At the power spectrum threshold P hold The weak signal below can be regarded as the nonlinear distortion amount, and thus a substantially distributed frequency band of nonlinear distortion can be obtained.
In order to suppress or even eliminate the nonlinear distortion amount of the compensated digital receiver 10, in step S145, the step of performing iterative operation on the compensation kernel vector of the compensation model by using the least square method based on the nonlinear signal power includes: and constructing an autocorrelation matrix according to the cost function by taking an operation target of the minimized nonlinear signal power as a cost function of a least square method, and carrying out iterative operation on a compensation kernel vector of the compensation model by adopting the least square method based on an inverse matrix of the autocorrelation matrix. Because matrix inversion has limitations on some hardware implementations, the embodiment of the invention can replace calculation with the cross-correlation vector of the cost function by the inverse matrix of the autocorrelation matrix during specific operation. After the updated compensation kernel vector is obtained, the updated compensation kernel vector is loaded into a compensation model to eliminate nonlinear distortion.
The compensation method provided by the embodiment of the invention directly compensates the signal received by the digital receiver 10 without additionally adding an analog-to-digital converter ADC to acquire the original input data, can largely eliminate the nonlinear distortion of the digital receiver 10 without obtaining the original input signal, and can better improve the SFDR performance of the system without spurious dynamic range by identifying the frequency distribution of nonlinear components, constructing a filter to extract nonlinear quantity, constructing a cost function of nonlinear distortion quantity signal energy in the compensation signal and using the cost function to identify and update compensation model parameters.
In a second embodiment of the present invention, a compensator for nonlinear distortion of a pulse field source is provided, as shown in fig. 3, and includes a nonlinear simulation module 111, a compensation model construction module 112, a compensation output acquisition module 113, and a real-time cancellation module 114. The nonlinear simulation module 111 is configured to construct a Volterra series model for nonlinear distortion of the analog digital receiver 10 by using the Volterra series as a functional series, and load a signal to be compensated received by the digital receiver 10 into the Volterra series model to obtain a distortion signal, where the distortion signal carries a nonlinear distortion amount. The compensation model construction module 112 is configured to construct a compensation model for characterizing the nonlinear distortion according to the Volterra series model, where the compensation model includes a nonlinear memory matrix and a compensation kernel vector, the nonlinear memory matrix is formed by memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vector is formed by kernel coefficients of each order of the Volterra series model. The compensation output obtaining module 113 is configured to obtain a compensation output by eliminating the nonlinear distortion amount in the distortion signal using the compensation model. The real-time cancellation module 114 is configured to update the compensation kernel vector of the compensation model by using a least square method based on the compensation output, so as to cancel the nonlinear distortion amount in the distortion signal in real time by using the compensation model updated by the compensation kernel vector.
The real-time cancellation module 114 mainly includes an extraction unit 143, a determination unit 144, and an iterative operation unit 145, where the extraction unit 143 is configured to extract a nonlinear distortion component from the compensation output, where the nonlinear distortion component is a value of a nonlinear distortion amount in a distortion signal obtained by loading a k-th signal to be compensated into the Volterra series model. The determining unit 144 is configured to determine the nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components. The iterative operation unit 145 is configured to perform iterative operation on the compensation kernel vector of the compensation model by using a least square method based on the nonlinear signal power, obtain a compensation kernel vector with updated data, and load the updated compensation kernel vector into the compensation model, so as to eliminate the nonlinear distortion in the distortion signal in real time by using the compensation model.
In a specific application example of the embodiment of the present invention, the real-time cancellation module 114 further includes a frequency band determining unit 141 and a frequency band determining unit 141, where the frequency band determining unit 141 is configured to determine a power spectral density of the distorted signal, and determine frequency band information of the nonlinear distortion amount according to the power spectral density. The filter construction unit 142 is configured to construct a multi-pass filter based on the band information. An extracting unit 143, configured to extract a nonlinear distortion component from the compensation output by using the multi-passband filter configured by the filter configuration unit 142, where the nonlinear distortion component is a value of a nonlinear distortion amount in a distortion signal obtained by loading the kth signal to be compensated into the Volterra series model. The determining unit 144 is configured to determine the nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components. The iterative operation unit 145 is configured to perform iterative operation on the compensation kernel vector of the compensation model by using a least square method based on the nonlinear signal power, obtain a compensation kernel vector with updated data, and load the updated compensation kernel vector into the compensation model, so as to eliminate the nonlinear distortion in the distortion signal in real time by using the compensation model.
In a third embodiment of the invention, a signal compensation system is provided, as shown in fig. 4, comprising a pulsed field source 20 for generating a pulsed radiation signal and a digital receiver 10, the digital receiver 10 comprising a compensator 11 for nonlinear distortion of the digital receiver 10. The digital receiver 10 receives the pulse radiation signal and loads the pulse radiation signal as a signal to be compensated into the compensator 11 facing the nonlinear distortion of the digital receiver 10, thereby improving the linearity of the radiation signal of the pulse field source 20 in a manner of improving the linearity of the digital receiver 10 without adding an additional analog-to-digital converter ADC.
The digital receiver 10 further comprises a radio frequency front end 13 and an analog-to-digital converter 12, the input of the analog-to-digital converter 12 being electrically connected to the output of the radio frequency front end 13, the output of the analog-to-digital converter 12 being electrically connected to a compensator 11 facing the nonlinear distortion of the digital receiver 10. The radio frequency front end 13 receives the pulse radiation signal generated by the pulse field source 20 and sends the pulse radiation signal to the analog-to-digital converter 12, the analog-to-digital converter 12 converts the pulse radiation signal into a digital signal and then sends the digital signal to the compensator 11 as a signal to be compensated for compensation, so that nonlinear distortion in the pulse radiation signal is eliminated, and the spurious-free dynamic range SFDR performance of the signal compensation system is improved.
The fourth embodiment of the present invention combines the above three embodiments and fig. 1 to 9, and proposes an application embodiment for restoring a received signal by eliminating nonlinear distortion components of the signal by using a compensation model based on a post-compensation model construction method of a least squares method, aiming at nonlinear distortion problems existing in a wideband digital receiver 10.
The embodiment of the invention discloses a post-compensation model construction method based on a least square method, which comprises the following specific steps:
step one, a suitable nonlinear model is selected to simulate nonlinear distortion between the rf front end 13 and the analog-to-digital converter 12 (in the embodiment of the present invention, the analog-to-digital converter 12 is hereinafter abbreviated as ADC) of the digital receiver 10, and the original signal is loaded to obtain a distorted signal, as shown in fig. 5.
Since most transceivers have some memory effect, the memory effect is particularly pronounced for wideband digital receiver 10. In the embodiment of the invention, a discrete Volterra series model is selected to simulate nonlinear distortion of the radio frequency front end 13 and the analog-to-digital converter 12 of the digital receiver 10, and the expression of the Volterra series model is as follows:
where x (k) is the kth discrete input signal (i.e., the kth discrete signal to be compensated of the input), y (k) is x (k is the corresponding output of the Volterra series model, D is the nonlinear order of the input, D is the maximum of D, N d Is the maximum memory depth of the d-th order Volterra kernel, h (r 1 ，r 2 ，…，r d ) Is the kernel coefficient corresponding to the d-th order Volterra. The total number of model kernel coefficients is:
in practice, as the memory depth increases, the signal amplitude is attenuated, and the influence of the high-order nonlinear distortion of the attenuation post term on the system linearity is greatly reduced. For the digital receiver 10, the spurious-free dynamic range of the system is most affected by low-order nonlinear distortion. Therefore, the Volterra series model of the low-order short memory effect can be adopted to simulate the system distortion. For example, taking the Volterra series model with maximum memory depth of 2 for the 2 nd and 3 rd order terms simulates the nonlinear distortion of the digital receiver 10.
And step two, identifying the strong signal and the distortion signal. For the entire receive frequency band of the wideband digital receiver 10, the received strong signal is typically distributed sparsely, while the nonlinear distortion component of the receiver system can be considered to be mainly due to the strong signal. N-point discrete Fourier transform is obtained on the distortion signal, and the power spectrum density of the distortion signal is obtained. The power spectral density expression is:
drawing a power spectrum density diagram of a distortion signal, and setting a proper power spectrum threshold P hold It can be considered that the power spectral density in the first nyquist band in the power spectral density plot exceeds the threshold P hold Is a strong signal to be received. At threshold P hold The weak signal below can be regarded as a nonlinear distortion amount, and thus a substantially distributed frequency band of the nonlinear distortion amount can be obtained. Constructing a multi-pass filter from frequency information (or frequency band information) of nonlinear distortion amount to obtain filter coefficients, and defining an FIR tap coefficient vector of the filter coefficients as g= [ g ] 0 g 1 …g G-1 ] T The matrix dimension is G x l, T is the transpose operator.
And thirdly, the compensation model of the nonlinear distortion quantity should simulate the nonlinear distortion of the system as far as possible, so that the compensation model adopts a structure similar to the Volterra series model in the first step to represent the nonlinear memory part. The expression of the compensation model B (k) is
B(k)＝A T (k)×w
Defining a matrix A characterizing the nonlinear memory portion of the compensation system T (k) Is a G-N matrix
A(k)＝[v(k-G+1)v(k-G+2)…v(k-1)v(k)]
Defining a memory nonlinear column vector in which v (k) is a linear term-free per-order constructed from y (k), w being a compensation kernel vector of a compensation model to be identified.
v(k)＝[y 2 (k)y(k)y(k-1)…y 2 (k-N d +1)y 3 (k)y 2 (k)y(k-1)…y D (k-N d +1)] T 。
w＝[h(0，0)h(0，1)…h(N 2 -1，N 2 -1)h(0，0，0)h(0，0，1)…h(N D -1，…，N D -1)] T 。
The compensation output obtained after compensation is: s (k) =y (k) -B (k), wherein:
y(k)＝[y(k-G+1)y(k-G+2)…y(k-1)y(k)] T
the compensation algorithm provided by the embodiment of the invention aims to inhibit or even eliminate the nonlinear distortion of the compensated system. In this regard, embodiments of the present invention extract the kth nonlinear component value from the compensation output s (k):
s f (k，w)＝g T ×s(k)＝g r ×[y(k)-B(k)]。
And step four, when the kernel vector calculation of the compensation model is performed, the nonlinear component energy value of the compensation model is required to be the minimum.
Step four, performing iterative calculation on the kernel vector of the compensation model by a least square method:
the extracted nonlinear signal power is represented by the sum of squares of the resulting K discrete data (nonlinear component values), and the nonlinear signal power (or signal energy) can be represented as:
wherein the method comprises the steps of
Representing the short-term energy of the compensated output of the digital receiver 10 per unit time in which the kth discrete input signal is located. Taking the minimum value of P (w) as the cost function of the least square method, an iterative formula can be obtained:
w(i)＝w(i-1)-{Q T [w(i-1)]*Q[w(i-1)]}-1*Q T [w(i-1)]*s f [w(i-1)]；
wherein Q (w) is a K x N-dimensional first derivative matrix. The iterative formula can be further expressed as:
The least square method RLS algorithm has high convergence rate, so that the parameter identification capability can be improved on the premise of real-time change of an input signal. The cost function can be written as:
where τ is a forgetting factor, 0< τ <1, τ is related to the rate of change of the nonlinear distortion of the system.
The autocorrelation matrix R (i) and the cross-correlation vector M (i) are obtained as:
R(i)＝τR(i-1)+V(k)gg T V T (k)
M(i)＝τM(i-1)+g T y(k)V(k)g
the update formula of the derived compensation kernel vector is:
w(i)＝w(i-1)-R -1 (i){[V(k)gg T V T (k)]w(i-1)-V(k)gg T y(k))}
Since matrix inversion has limitations on some hardware implementations, R can also be assumed -1 (i) =m (i) instead of calculation.
And finally, loading the nuclear coefficient of the updated compensation nuclear vector into a compensation model to finish nonlinear distortion cancellation, wherein the specific cancellation principle is shown in figure 5.
Therefore, the embodiment of the invention has the advantages that: the nonlinear distortion component can be extracted by identifying the frequency distribution of nonlinear distortion, constructing a filter, constructing a nonlinear energy cost function in a compensation signal and identifying compensation model parameters by the nonlinear distortion component, and the nonlinear distortion of the system can be eliminated in a large amount under the condition that an additional ADC is not required to be added to acquire an actual input signal, so that the SFDR performance of the system without spurious dynamic range can be improved well.
The invention provides a post-compensation model construction method based on a least square method aiming at the problem that nonlinear distortion exists in a broadband digital receiver 10 to influence the SFDR of a system spurious-free dynamic range.
As shown in fig. 6, for convenience of explanation, the reliability of the post-compensation model for nonlinear distortion cancellation is verified by using a binaural signal as a simulation experiment, and the specific steps are as follows:
where fs is the sampling frequency, set to 100MHZ. f (f) 1 、f 2 For the duplex signal frequencies, here set to 6.3MHZ and 10.3MHZ, respectively. Taking the maximum memory depth of the 2 nd and 3 rd order items of the Volterra series as 2, and constructing a nonlinear series model as follows:
y(k)＝Y(k)*W
Y(k)＝[x(k)，x 2 (k)，x(k)x(k-1)，x 2 (k-1)，x 3 (k)，x 2 (k)x(k-1)，x(k)x 2 (k-1)，x 3 (k-1)]，W＝10 -2 ×[100，0.028，1.232，0.667，0.923，1.188，1.383，1.107]。
w is a kernel vector of the nonlinear series model, and x (k) is substituted into the model to obtain a distorted output signal y (k) of the receiver.
And 2, drawing a power density diagram, setting a threshold value, and identifying a strong signal and a distortion signal. For the whole receiving frequency band of the wideband receiver, the received strong signal is generally distributed sparsely, while the nonlinear distortion component of the receiver system can be considered to be mainly brought by the strong signal. The signal was subjected to N-point discrete Fourier transform to obtain the power spectral density of the signal, and the power spectral density map was plotted as shown in FIG. 7.
As can be seen from fig. 7, a plurality of distortion components are generated after the signal is distorted, and the power values of these quantities are large. The maximum spurious-free power spectrum range SFDR of the system is only 31dB.
And 3, approximately estimating the frequency distribution position of the distorted signal from the power spectrogram, constructing a multi-pass filter from the nonlinear distorted frequency information, and deriving the filter coefficient. The FIR tap coefficient vector is defined as g= [ g ] 0 g 1 …g G-1 ] T The dimension is G1. Here the coefficient length of the filter is set to 150.
And 4, performing iterative calculation on the kernel vector of the compensation model by a least square method. The entire adaptive parameter update process is shown in fig. 8.
Constructing an autocorrelation matrix R (i): r (i) =τr (i-1) +v (k) gg T V T (k)。
And updating compensation model parameters by using a least square method RLS algorithm, wherein the training times are set to be 200 times.
w(i)＝w(i-1)-R -1 (i){[V(k)gg T V T (k)]w(i-1)-V(k)gg T y(k)}}
Finally, the calculated coefficients are updated into the compensation model B (k): b (k) =a T (k) X w, wherein:
A(k)＝[v(k-G+1)v(k-G+2)…v(k-1)v(k)]。
v(k)＝[y 2 (k)y(k)y(k-1)…y 2 (k-N d +1)y 3 (k)y 2 (k)y(k-1)…y D (k-N d +1)] T 。
the spectrum of the compensated signal is shown in fig. 9.
It can be found from fig. 9 that the nonlinear distortion is well suppressed, and after the compensation is performed by the method, the SFDR value of the system is improved from original 32dB to 53dB, and the improvement of 22dB is achieved. In addition, the linearization effect of the scheme is measured by adopting a normalized mean square error NMSE. The mathematical expression is as follows:
wherein y is r (i) For actually inputting data, y is taken as a standard reference signal s (i) And the compensation signal is output after the compensation of the scheme, and K is the number of output data participating in calculation. The NMSE compares to compensate for the deviation of the input signal from the actual signal, so the smaller the value the better. By calculation, the NMSE of the system before compensation is minus 20dB, and the NMSE of the system after compensation is greatly reduced to minus 40dB. The invention has the advantages that the signal receiving quality is greatly improved, and the nonlinear distortion of the output signal of the pulse field source is improved.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are merely for convenience of distinguishing the corresponding components, and unless otherwise stated, the terms have no special meaning, and thus should not be construed as limiting the scope of the present application.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for compensating nonlinear distortion of a pulsed field source, comprising:
a Volterra series model for nonlinear distortion of an analog digital receiver (10) is constructed by adopting the Volterra series as a functional series, and a signal to be compensated received by the digital receiver (10) is loaded into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries nonlinear distortion quantity;
constructing a compensation model for representing the nonlinear distortion quantity according to the Volterra series model, wherein the compensation model comprises a nonlinear memory matrix and a compensation kernel vector, the nonlinear memory matrix is composed of memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vector is composed of kernel coefficients of each order of the Volterra series model;
The nonlinear distortion amount in the distortion signal is eliminated by using the compensation model, and compensation output is obtained;
based on the compensation output, updating the compensation kernel vector of the compensation model by adopting a least square method so as to eliminate the nonlinear distortion quantity in the distortion signal in real time by utilizing the compensation model updated by the compensation kernel vector.
2. The method of compensating for nonlinear distortion of a pulsed field source of claim 1, wherein updating the compensation kernel vector of the compensation model using a least squares method based on the compensation output comprises:
extracting a nonlinear distortion component from the compensation output, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
determining nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
3. The method of compensating for nonlinear distortion of a pulsed field source of claim 1, wherein updating the compensation kernel vector of the compensation model using a least squares method based on the compensation output further comprises:
determining the power spectral density of the distorted signal, and determining the frequency band information of the nonlinear distortion amount according to the power spectral density;
constructing a multi-passband filter according to the frequency band information;
extracting the nonlinear distortion component from the compensation output by adopting the multi-pass filter, wherein the nonlinear distortion component is a value of the nonlinear distortion quantity in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
determining nonlinear signal power of the nonlinear distortion amount according to the extracted k nonlinear distortion components;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
4. A compensation method for nonlinear distortion of a pulsed field source as defined in claim 3 wherein determining a power spectral density of the distorted signal and determining frequency band information for the amount of nonlinear distortion from the power spectral density comprises:
performing discrete Fourier transform on the distorted signal to obtain the power spectrum density, and drawing a power spectrum density diagram according to the power spectrum density;
and determining the power spectrum density below the power spectrum threshold as the power spectrum density of the nonlinear distortion amount in the first Nyquist frequency band in the power spectrum density diagram based on a preset power spectrum threshold.
5. A compensation method for pulsed field source nonlinear distortion according to claim 2 or 3, wherein the step of iteratively operating the compensation kernel vector of the compensation model using a least squares method based on the nonlinear signal power comprises:
taking an operation target for minimizing the nonlinear signal power as a cost function of the least square method, and constructing an autocorrelation matrix according to the cost function;
and carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on an inverse matrix of the autocorrelation matrix.
6. A compensator for pulsed field source nonlinear distortion, comprising:
the nonlinear simulation module (111) is used for constructing a Volterra series model for simulating nonlinear distortion of the digital receiver (10) by adopting the Volterra series as a functional series, and loading a signal to be compensated received by the digital receiver (10) into the Volterra series model to obtain a distortion signal, wherein the distortion signal carries nonlinear distortion quantity;
a compensation model construction module (112) for constructing a compensation model for representing the nonlinear distortion amount according to the Volterra series model, wherein the compensation model comprises a nonlinear memory matrix and a compensation kernel vector, the nonlinear memory matrix is composed of memory nonlinear column vectors of each order constructed by the Volterra series model, and the compensation kernel vector is composed of kernel coefficients of each order of the Volterra series model;
a compensation output acquisition module (113) for eliminating the nonlinear distortion amount in the distortion signal by using the compensation model to obtain a compensation output;
and the real-time elimination module (114) is used for updating the compensation kernel vector of the compensation model by adopting a least square method based on the compensation output so as to eliminate the nonlinear distortion quantity in the distortion signal in real time by utilizing the compensation model updated by the compensation kernel vector.
7. The compensator for pulsed field source nonlinear distortion of claim 6, wherein the real-time cancellation module (114) comprises:
an extracting unit (143) configured to extract a nonlinear distortion component from the compensation output, where the nonlinear distortion component is a value of the nonlinear distortion amount in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
a determining unit (144) for determining a nonlinear signal power of the nonlinear distortion amount from the extracted k nonlinear distortion components;
and the iterative operation unit (145) is used for carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
8. The compensator for pulsed field source nonlinear distortion of claim 6, wherein the real-time cancellation module (114) further comprises:
a frequency band determining unit (141) for determining a power spectral density of the distorted signal and determining frequency band information of the nonlinear distortion amount according to the power spectral density;
A filter construction unit (142) for constructing a multi-pass filter from the band information;
an extracting unit (143) configured to extract the nonlinear distortion component from the compensation output by using the multi-passband filter, where the nonlinear distortion component is a value of the nonlinear distortion amount in the distortion signal obtained by loading the kth signal to be compensated into the Volterra series model, and k is a positive integer;
a determining unit (144) for determining a nonlinear signal power of the nonlinear distortion amount from the extracted k nonlinear distortion components;
and the iterative operation unit (145) is used for carrying out iterative operation on the compensation kernel vector of the compensation model by adopting a least square method based on the nonlinear signal power to obtain the compensation kernel vector with updated data, and loading the updated compensation kernel vector into the compensation model so as to eliminate the nonlinear distortion in the distortion signal in real time by utilizing the compensation model.
9. A signal compensation system, comprising:
a digital receiver (10), the digital receiver (10) comprising a compensator (11) for pulsed field source nonlinear distortion as claimed in any one of claims 6 to 8;
-a pulsed field source (20), the pulsed field source (20) being adapted to generate a pulsed radiation signal;
the digital receiver (10) receives the pulsed radiation signal and loads the pulsed radiation signal as a signal to be compensated into the digital receiver nonlinear distortion-oriented compensator (11).
10. The signal compensation system of claim 9 wherein said digital receiver further comprises:
a radio frequency front end (13);
and the input end of the analog-to-digital converter (12) is electrically connected with the output end of the radio frequency front end (13), and the output end of the analog-to-digital converter (12) is electrically connected with the compensator (11) facing the nonlinear distortion of the digital receiver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211678223.4A CN116318210A (en) | 2022-12-26 | 2022-12-26 | Compensation method, compensator and system for nonlinear distortion of pulse field source |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211678223.4A CN116318210A (en) | 2022-12-26 | 2022-12-26 | Compensation method, compensator and system for nonlinear distortion of pulse field source |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116318210A true CN116318210A (en) | 2023-06-23 |
Family
ID=86815644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211678223.4A Pending CN116318210A (en) | 2022-12-26 | 2022-12-26 | Compensation method, compensator and system for nonlinear distortion of pulse field source |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116318210A (en) |
-
2022
- 2022-12-26 CN CN202211678223.4A patent/CN116318210A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10623118B2 (en) | Modular multi-channel RF calibration architecture for linearization | |
| US8836426B2 (en) | Adaptive linearizer with narrowband feedback path | |
| US20150214904A1 (en) | Distortion compensation apparatus and distortion compensation method | |
| US7412469B2 (en) | Power amplifier pre-distorter training | |
| JP2000031748A (en) | Feedforward linearization device | |
| CN113949350A (en) | Digital predistortion method and system based on baseband-radio frequency joint optimization | |
| US11239854B2 (en) | Non-linearity correction | |
| Peng et al. | Design and implementation of software-defined radio receiver based on blind nonlinear system identification and compensation | |
| CN113037226B (en) | Digital predistortion design method and device based on adaptive step length clipping method | |
| US11251803B2 (en) | Non-linearity correction | |
| Schoukens et al. | Obtaining the preinverse of a power amplifier using iterative learning control | |
| US7924942B2 (en) | Power amplifier pre-distortion | |
| US10666307B2 (en) | Compact model nonlinear compensation of bandlimited receiver systems | |
| Michev et al. | Adaptive compensation of hardware impairments in digitally modulated radars using ML-based behavioral models | |
| Silva et al. | Optimal-filter approach for nonlinear power amplifier modeling and equalization | |
| CN110971197B (en) | Digital predistortion device and method | |
| CN116318210A (en) | Compensation method, compensator and system for nonlinear distortion of pulse field source | |
| Pillai et al. | Implementation of digital pre-distortion for power amplifier linearisation in software defined radio | |
| Braithwaite | Memory correction of a Doherty power amplifier with a WCDMA input using digital predistortion | |
| US10715191B2 (en) | Method for characterizing nonlinear distortion of transmitter, associated transmitter and characterization circuit thereof | |
| CN117335820A (en) | Method for improving linearity of high-power pulse field source | |
| CN112953868B (en) | Predistortion method, apparatus, computer device and readable storage medium | |
| RU2786412C1 (en) | Method for compensating nonlinear distortions of high-frequency power amplifiers and a device for its implementation | |
| CN115811320B (en) | Method, device and medium for eliminating CIM3 intermodulation interference signal of zero intermediate frequency transmitter | |
| Liang et al. | Post Compensation of Weak Nonlinearity for Digital Receiver |
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 |