CN114629756A - Adaptive predistortion method and system for multimode 5G broadcast transmitter - Google Patents
Adaptive predistortion method and system for multimode 5G broadcast transmitter Download PDFInfo
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Abstract
The invention provides a self-adaptive predistortion method and a system for a multimode 5G broadcast transmitter, which comprises the following steps: the predistortion system realizes the synchronization of the sampling signal of the power amplifier and the original signal under different baseband signal sampling rates through oversampling; modifying a memory polynomial model of the self-adaptive algorithm module to ensure that the nonlinear orders corresponding to each memory term in the memory polynomial model are different; and then, by calculating the normalized least square fitting error of the output signal of the self-adaptive algorithm module, the proper maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory item under the maximum memory depth are searched. The invention can be suitable for 5G broadcast transmitters with different baseband signal sampling rates, different working frequency bands and different signal bandwidths.
Description
Technical Field
The invention relates to the technical field of 5G broadcast transmitters, in particular to a method and a system for self-adaptive predistortion of a multimode 5G broadcast transmitter.
Background
The 5G Broadcasting System (5G Broadcasting System) is a television System constructed by using a broadcast transmission technology compliant with the requirements of the mobile communication standard established by the 3GPP (international telecommunication standardization organization). The standard establishment time interval is in the 5G standard establishment working cycle of 3GPP, and the characteristic meets the 5G technical performance requirement. The 3GPP adds a 5G broadcast technology based on a broadcast tower in Release16, so as to implement a large tower range signal coverage to make up for a point-to-point transmission short board of the 5G communication technology through the 5G broadcast technology. By developing the 5G broadcast television service, the broadcast television content can be transmitted to the mobile phone terminal in a broadcast transmission mode through the broadcast television tower and the mobile communication base station, a user can watch broadcast television programs or receive data push service at any time and any place, and any audio and video data communication flow is not consumed. By applying the 5G broadcast television technology, 7000 seats of broadcast television wireless transmission tower resources in the whole country can be survived, the full coverage of broadcast television signals to a 5G mobile phone is realized, a large-size television which is fixedly received in the prior art can be covered, a small-size mobile terminal can be supported, and the communication of the broadcast television terminal and the communication of people can be realized comprehensively. The 5G broadcast television service can fully play the advantages of low delay and high transmission efficiency of a broadcast network, and simultaneously, a novel broadcast television service is constructed by depending on the flexible characteristics of a mobile communication network, so that the development of a new era is adapted. The broadcast television organizations and operators in various countries can upgrade and update the infrastructure by using the 5G broadcast television technology, so that the broadcast television programs can be smoothly transmitted to the 5G mobile communication terminal, and the expanded coverage of the broadcast television service is realized.
As a core device of the 5G broadcasting system, 5G broadcasting transmitters of different power classes are applied to a broadcast television tower and a 5G mobile communication base station. As an important component of a 5G broadcast transmitter, a power amplifier is responsible for amplifying a radio frequency signal to a required power. Due to the inherent characteristics of electronic components, the input and output signals of the power amplifier generally have a nonlinear relationship. Due to the fact that the technology adopted in modern communication such as OFDM and QAM mapping is adopted, the 5G radio frequency signal is a non-constant envelope signal which is very sensitive to nonlinearity of a power amplifier, after the 5G radio frequency signal is amplified by the power amplifier, not only can a harmonic signal be generated, but also serious out-of-band frequency spectrum leakage and in-band distortion can be generated, the quality of a transmitted signal is seriously influenced, adjacent channel interference and the receiving threshold of a receiving end are improved, and the transmission distance is reduced. In order to reduce the nonlinear distortion of a power amplifier and improve the efficiency of the power amplifier, the digital baseband predistortion technology is a linearization method widely adopted at present, and the core idea is to introduce a nonlinear transformation predistortion system before a signal enters the power amplifier, wherein the characteristic of the nonlinear transformation predistortion system and the nonlinear amplification characteristic of the power amplifier are in an inverse function relationship. After the signal after predistortion is amplified by a power amplifier, the output signal and the original signal before predistortion are in a linear relation.
Unlike traditional broadcast signals, 5G broadcasts have the characteristic of configurable bandwidth of radio frequency signals, and the bandwidths specified by 3GPP in Release16 Release are 5MHz, 10MHz, 15MHz, 20MHz, and the like. Different signal bandwidths, corresponding to different baseband symbol rates and different sampling rates, are also possible. According to the national planning of communication frequency bands, the 5G broadcast can work on two frequency bands of 700MHz and 4.9 GHz. And the characteristics of the corresponding power amplifier module of the working signal and the signal with different bandwidths on different frequency bands have larger difference. If the predistortion processing is performed, the parameters of the predistorter also have a large difference.
Disclosure of Invention
The invention aims to provide a self-adaptive predistortion method and a self-adaptive predistortion system for a multimode 5G broadcast transmitter, wherein the self-adaptive predistortion method and the self-adaptive predistortion system can be suitable for 5G broadcast transmitters with different baseband signal sampling rates, different working frequency bands and different signal bandwidths.
The invention provides a self-adaptive predistortion method of a multimode 5G broadcast transmitter, which comprises the following steps:
the predistortion system realizes the synchronization of the sampling signal of the power amplifier and the original signal under different baseband signal sampling rates through oversampling;
modifying a memory polynomial model of the self-adaptive algorithm module to ensure that the nonlinear orders corresponding to each memory term in the memory polynomial model are different; and then, by calculating the normalized least square fitting error of the output signal of the self-adaptive algorithm module, the proper maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory item under the maximum memory depth are searched.
Further, the method for synchronizing the sampling signal of the power amplifier and the original signal by the predistortion system through oversampling includes:
the integer multiple of the least common multiple of the sampling rate of the baseband signals is set as the oversampling rate of the predistortion system to perform oversampling, so that the synchronization of the sampling signals of the power amplifier and the original signals under different baseband signal sampling rates is realized.
Further, the oversampled sampling signal y (n) is used to be expressed as:
y(n)=p(R*n+D)
wherein p (n) is an output signal of the analog-to-digital converter ADC; r is an oversampling ratio; d is the sample point offset.
Further, the method for obtaining the sampling point offset D is as follows:
firstly, the cross-correlation value r of the sampling signal and the original signal is obtainedzp(d)：
Wherein d represents the sample point offset at the time of oversampling; z is a radical of*(n) represents the conjugate sequence of the original signal z (n); n is a sampling point; n is the number of sampling points;
then the cross-correlation value r is calculatedzp(d) D when the maximum value is taken is D.
Further, after modifying the memory polynomial model of the adaptive algorithm module, the output signal v (n) of the adaptive algorithm module is represented as:
wherein m is the memory depth of the memory polynomial model, P is the nonlinear order of the memory polynomial model, P ismThe memory depth M is corresponding to the nonlinear order of the memory item, and M-1 is the maximum memory depth; k is a radical of formulap,mIs the coefficient of the memory polynomial model; y (n-m) is a sequence of y (n) delayed by m sample points.
Further, the normalized least squares fitting error E of the output signal of the adaptive algorithm module is represented as:
wherein e (n) is the error between the output signal v (n) of the adaptive algorithm module and the original signal z (n).
Further, the method for finding the suitable maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory item under the maximum memory depth by calculating the normalized least square fitting error of the output signal of the adaptive algorithm module comprises the following steps:
v(n)=k0,0y(n)+k1,0y(n)|y(n)|1+k2,0y(n)|y(n)|2
calculating a normalized least squares fit error E (P)0)=E(3)；
Step 2, let PM-1=PM-1+1, that is, adding 1 to the non-linear order corresponding to the memory term with memory depth M-1, and calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-1)；
step 4, let M = M +1, PM-1=3, i.e. adding 1 memory term, and making the nonlinear order corresponding to the added memory term 3, then calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-2,PM-1)=E(P0,P1,…,PM-2,3)；
Step 5, if the E (P) calculated in step 4 is obtained0,P1,…,PM-2,3)≤α*E(P0,P1,…,PM-2) If yes, skipping to the step 2; otherwise, making M = M-1, namely deleting the memory item added in the step 4, and ending the calculation process.
Preferably, the value range of the error tolerance constant alpha is [0.8,0.95 ].
The invention also provides a self-adaptive predistortion system of the multimode 5G broadcast transmitter, and the predistortion system adopts the self-adaptive predistortion method of the multimode 5G broadcast transmitter to realize the predistortion of the power amplifier.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
on one hand, the invention enables the predistortion system to be suitable for 5G broadcast transmitters with different baseband signal sampling rates through oversampling, and on the other hand, enables the predistortion system to be suitable for 5G broadcast transmitters with different working frequency bands and different signal bandwidths through multimode self-adaptation. Therefore, the invention can be suitable for 5G broadcast transmitters with different baseband signal sampling rates, different working frequency bands and different signal bandwidths.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic diagram of an adaptive predistortion system of a multimode 5G broadcast transmitter in an embodiment of the invention.
FIG. 2 is a flowchart illustrating the steps of finding the proper maximum memory depth and the non-linear order corresponding to each memory entry under the maximum memory depth according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, the predistortion system includes an adaptive algorithm module a using a memory polynomial model, and a predistorter, a digital-to-analog converter DAC, a power amplifier HPA, a power amplifier gain G, an analog-to-digital converter ADC, and the like formed by copying the adaptive algorithm module a. For such predistortion system, the present embodiment proposes an adaptive predistortion method for a multimode 5G broadcast transmitter, including:
(1) oversampling: the predistortion system realizes the synchronization of the sampling signal of the power amplifier and the original signal under different baseband signal sampling rates through oversampling;
(2) multimode self-adaptation: modifying a memory polynomial model of the self-adaptive algorithm module to ensure that the nonlinear orders corresponding to each memory term in the memory polynomial model are different; and then, by calculating the normalized least square fitting error of the output signal of the self-adaptive algorithm module, the proper maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory item under the maximum memory depth are searched.
The principle of the adaptive predistortion method of the multimode 5G broadcast transmitter is as follows:
(1) oversampling
In an actual digital baseband predistortion system, since a digital baseband signal is processed by a digital-to-analog converter (DAC), a power amplifier (HPA) and an analog-to-digital converter (ADC) to bring a certain time delay, the predistortion system needs to perform time delay calibration on a sampling signal and an original signal; due to the phase difference of the local oscillation signals of the up-conversion and the down-conversion, the phase difference of the sampling signal and the original signal also exists, and the phase calibration needs to be carried out on the sampling signal and the original signal; due to gain uncertainty of the whole signal loop, the sampled signal and the original signal have amplitude difference, and the amplitude of the sampled signal and the original signal needs to be calibrated.
Generally, the calibration delay method is to obtain a value D of D when the cross-correlation value between a sampling signal y (n) and an original signal z (n) is maximized, wherein the sequence z (n-D) delays D sampling points; the cross-correlation value rzy(d) Expressed as:
wherein n is a sampling point; n is the number of sampling points; z is a radical of*(n-d) represents a conjugate sequence of z (n-d).
Due to the discreteness of the predistortion system, the sequence z (n-d) after the time delay calibration and the sampling signal y (n) have a slight time difference Δ T at the actual sampling time, and the sampling interval time is T, and the time delay difference can be expressed as:
Z((n-d)T)=y(nT±Δt)
wherein, Delta T is less than or equal to T/2. The presence of Δ t is equivalent to introducing a memory effect value for the predistortion system. In order to obtain the power amplifier model more accurately, the value of Δ t should be reduced as much as possible. And the lower the sampling frequency is, the larger the sampling time interval is, and the larger the power amplification model error is obtained.
On the other hand, since the baseband signal of the 5G broadcast signal has a variable bandwidth and a variable sampling rate, according to the predistortion system, the analog-to-digital converter ADC should have a variable sampling rate to match the sampling rate of the 5G broadcast baseband signal, or the analog-to-digital converter ADC with a fixed sampling rate should match the sampling rate of the rate baseband signal by extracting the sampling value. The design of the variable sampling rate ADC increases the complexity of the predistortion system design, while the phase uncertainty of the decimation operation using the fixed sampling rate ADC increases the predistortion system error.
Based on the above two reasons, the method for synchronizing the sampling signal of the power amplifier and the original signal under different baseband signal sampling rates by the predistortion system through oversampling in this embodiment includes:
the integer multiple of the least common multiple of the sampling rate of the baseband signal is set as the oversampling rate of the predistortion system to perform oversampling, so that the synchronization of the sampling signal of the power amplifier and the original signal is realized, and the synchronization error is reduced. That is, the sampling rate of the baseband signal x (n) is set tofs 0,fs 1,…,fs L Selectingfs 0,fs 1,…,fs L Is used as the sampling rate of the predistortion systemfs R Corresponding to oversampling ratios R ofR 0,R 1,…,R L . Under the condition of over-sampling, the output signal of the analog-to-digital converter ADC is p (n), and then the cross-correlation value r of the sampling signal and the original signal is calculatedzp(d)：
Wherein d represents the sample point offset at the time of oversampling; z is a radical of*(n) represents the conjugate sequence of the original signal z (n); n is a sampling point; n is the number of sampling points;
then the cross-correlation value r is calculatedzp(d) D when the maximum value is taken is D.
Thus, the oversampled sampling signal y (n) is expressed as:
y(n)=p(R*n+D)
wherein p (n) is an output signal of the analog-to-digital converter ADC; r is an oversampling ratio; d is the sample point offset.
It can be seen that with oversampling, the maximum deviation delay difference of the delay between the original signal z (n) and the sampled signal y (n) is changed from the time interval of the baseband sampling to the sampling interval of the oversampling, i.e. by 1 ≧ 4fsReduced to 1-RfsTherefore, the memory effect of the predistortion system introduced by the sampling time difference is greatly reduced. Meanwhile, for different baseband signal sampling rates, the predistortion system can select a uniform system sampling rate for processing, and the design complexity of the predistortion system is reduced.
(2) Multi-mode adaptation
As shown in fig. 1, the output signals for the predistorter and adaptive algorithm module a using the memory polynomial model are as follows:
the output signal z (n) of the predistorter is:
the output signal v (n) of the adaptive algorithm module a is:
wherein, P is the nonlinear order of the memory polynomial model, and M is the memory depth of the memory polynomial model; k is a radical of formulap,mIs the coefficient of the memory polynomial model; y (n-m) is a sequence of y (n) delayed by m sample points.
For predistortion system, the extraction process of predistortion parameters is to solve the parameter k of memory polynomialp,mThe problem of minimizing the variance of the error | e (n) | of the adaptive algorithm module output signal v (n) with the original signal z (n) is solving the least squares solution of the following equation:
YK=Z
wherein:
the superscript "T" denotes the transpose of the matrix.
The nonlinearity of a power amplifier is generally related to the self-characteristics of the power amplifier, and different types of power amplifiers generally have different input and output characteristics. For example, when the amplitude of an input signal is small, the power amplifier gain of the transverse double-diffused metal oxide LDMOS power amplifier is a constant; and the power amplification gain of the GaN power amplifier to small signals can change along with the change of the signal amplitude. The corresponding nonlinear characteristics of the two power amplifiers have completely different curves. The memory effect of the power amplifier is mainly influenced by the matching characteristic of the power amplifier circuit, and the memory effect is more obvious when the matching performance of the circuit is worse. The wider the relative bandwidth of the transmitted signal, the larger the circuit mismatch in the pass band, and the more obvious the memory effect of the power amplifier. The working frequency of the 5G broadcast transmitter is 700MHz frequency band and 4.9GHz frequency band, and for different working frequency bands, the 5G broadcast transmitter selects power amplifiers with different nonlinear curves for signal amplification. The variable bandwidth of the 5G broadcast signal also influences the memory effect of the power amplifier.
As can be seen from the expression of the memory polynomial model, there are two parameters, i.e., the nonlinear order P and the memory depth M, and the corresponding memory polynomial model contains P × M coefficients. For the power amplifier with more complex nonlinear curve, a larger nonlinear order P needs to be selected for fitting. For power amplifiers with obvious memory effect, a deeper memory effect depth M is selected.
The non-linear order P and the memory depth M should be selected to satisfy the index requirement of the predistortion system. If the nonlinear order P and the memory depth M are too small, the error of the predistortion system is too large, and the distortion characteristic of the power amplifier cannot be well reflected, thereby causing the performance loss of the predistortion system. The selection of the nonlinear order P and the memory depth M is too large, which may cause the over-fitting problem of the predistortion system, and may not only cause the design complexity of the predistortion system, but also affect the stability and predistortion effect of the predistortion system.
Based on the above reasons, in this embodiment, the normalized least square fitting error of the output signal of the adaptive algorithm module is calculated to find the suitable maximum memory depth under different working frequency bands and different signal bandwidths, and the nonlinear order corresponding to each memory item under the maximum memory depth. Specifically, the method comprises the following steps:
and if the least square solution of the coefficient of the memory polynomial model is K, the normalized least square fitting error E of the output signal of the corresponding self-adaptive algorithm module is represented as:
wherein e (n) is the error between the output signal v (n) of the adaptive algorithm module and the original signal z (n).
And then modifying the classical memory polynomial model, namely modifying the memory polynomial model of the adaptive algorithm module, so that the nonlinear orders corresponding to each memory term in the memory polynomial model are different. When the memory depth of the memory polynomial model is m, the non-linear order of the corresponding memory term is PmAfter modifying the memory polynomial model of the adaptive algorithm module, the output signal v (n) of the adaptive algorithm module is expressed as:
wherein m is the memory depth of the memory polynomial model, P is the nonlinear order of the memory polynomial model, P ismThe memory depth M is corresponding to the nonlinear order of the memory item, and M-1 is the maximum memory depth; k is a radical of formulap,mIs the coefficient of the memory polynomial model; y (n-m) is a sequence of y (n) delayed by m sample points.
Let E (P)0,P1,…,PM-1) A normalized least squares fit error for the output signal v (n) of the adaptive algorithm module; wherein P is0For the non-linear order, P, corresponding to a memory term with a memory depth of 01For the non-linear order, P, corresponding to a memory term with a memory depth of 1M-1The order of the nonlinear term corresponding to the memory term with the maximum memory depth of M-1.
Therefore, as shown in fig. 2, the method for finding the suitable maximum memory depth and the nonlinear order corresponding to each memory term under the maximum memory depth by calculating the normalized least square fitting error of the output signal of the adaptive algorithm module comprises the following steps:
v(n)=k0,0y(n)+k1,0y(n)|y(n)|1+k2,0y(n)|y(n)|2
calculating a normalized least squares fit error E (P)0)=E(3)；
Step 2, let PM-1=PM-1+1, that is, adding 1 to the non-linear order corresponding to the memory term with memory depth M-1, and calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-1)；
Step 4, let M = M +1, PM-1=3, i.e. adding 1 memory term, and making the nonlinear order corresponding to the added memory term 3, then calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-2,PM-1)=E(P0,P1,…,PM-2,3)；
Step 5, if the E (P) calculated in step 4 is obtained0,P1,…,PM-2,3)≤α*E(P0,P1,…,PM-2) If yes, jumping to the step 2; otherwise, making M = M-1, namely deleting the memory item added in the step 4, and ending the calculation process.
Example (c):
the sampling rates of the baseband signals supporting the 5G broadcast signal of Release16 version of 3GPP are three, i.e., 7.68MHz, 15.36MHz, and 30.72MHz, corresponding to different signal bandwidths. The sampling rate of the predistortion system oversampling can be selected to be an integral multiple of 30.72MHz, and in order to give consideration to the requirements of device cost and predistortion system performance, 4 times of 30.72MHz, namely 122.88MHz, can be selected. Thus, the oversampling rates are 16 times, 8 times, and 4 times, respectively, corresponding to the sampling rates of the three baseband signals. Therefore, after the oversampling is adopted to synchronize the sampling signal y (n) of the power amplifier with the original signal z (n), the error of the sampling time is maximum T/2= (1/122.88MHZ)/2=4.069 ns.
And for the 700MHz frequency band, selecting a transverse double-diffused metal oxide LDMOS power amplifier as a power amplifier HPA of the predistortion system. When the signal bandwidth is 5MHz, the error tolerance constant α is selected to be 0.9, and P can be obtained by the method of the present embodiment for finding the appropriate maximum memory depth and the non-linear order corresponding to each memory item under the maximum memory depth0=7，P1=5, M = 2. When the signal bandwidth is 20MHz, P can be obtained0=7，P1=5，P2=5, M = 3. It can be seen from the number of parameters corresponding to the two signals with different bandwidths that the memory effect is more obvious when the signal bandwidth is wider. It can also be seen that the number of non-linear orders corresponding to a memory term with a memory depth different from zero can be selected to be less than the number of non-linear orders corresponding to a memory term with a memory depth of zero.
In summary, in the present invention, on one hand, the predistortion system can be applied to 5G broadcast transmitters with different baseband signal sampling rates through oversampling, and on the other hand, the predistortion system can be applied to 5G broadcast transmitters with different operating frequency bands and different signal bandwidths through multimode adaptation. Therefore, the embodiment also provides a self-adaptive predistortion system of a multimode 5G broadcast transmitter, the predistortion system adopts the self-adaptive predistortion method of the multimode 5G broadcast transmitter to realize predistortion of a power amplifier, and the predistortion system can be suitable for 5G broadcast transmitters with different baseband signal sampling rates, different working frequency bands and different signal bandwidths.
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 (9)
1. An adaptive predistortion method for a multimode 5G broadcast transmitter, comprising:
the predistortion system realizes the synchronization of the sampling signal of the power amplifier and the original signal under different baseband signal sampling rates through oversampling;
modifying a memory polynomial model of the self-adaptive algorithm module to ensure that the nonlinear orders corresponding to each memory term in the memory polynomial model are different; and then, by calculating the normalized least square fitting error of the output signal of the self-adaptive algorithm module, the proper maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory item under the maximum memory depth are searched.
2. The adaptive predistortion method of claim 1, wherein the method for synchronizing the sampling signal of the power amplifier with the original signal at different sampling rates of the baseband signal by the predistortion system through oversampling comprises:
the integer multiple of the least common multiple of the sampling rate of the baseband signals is set as the oversampling rate of the predistortion system to perform oversampling, so that the synchronization of the sampling signals of the power amplifier and the original signals under different baseband signal sampling rates is realized.
3. The adaptive predistortion method for 5G broadcast transmitter in multimode according to claim 2, wherein the oversampled sampled signal y (n) is represented by:
y(n)=p(R*n+D)
wherein p (n) is an output signal of the analog-to-digital converter ADC; r is an oversampling ratio; d is the sample point offset.
4. The adaptive predistortion method for a multimode 5G broadcast transmitter according to claim 3, wherein the sampling point offset D is obtained by:
firstly, acquiring a cross-correlation value r of a sampling signal and an original signalzp(d)：
Wherein d represents the sample point offset at the time of oversampling; z is a radical of*(n) represents the conjugate sequence of the original signal z (n); n is a sampling point; n is the number of sampling points;
then the cross-correlation value r is calculatedzp(d) D when the maximum value is taken is D.
5. The adaptive predistortion method for multimode 5G broadcast transmitter according to claim 4, wherein after modifying the memory polynomial model of the adaptive algorithm module, the output signal v (n) of the adaptive algorithm module is represented as:
wherein m is the memory depth of the memory polynomial model, P is the nonlinear order of the memory polynomial model, P ismThe memory depth M is corresponding to the nonlinear order of the memory item, and M-1 is the maximum memory depth; k is a radical of formulap,mIs the coefficient of the memory polynomial model; y (n-m) is a sequence of y (n) delayed by m sample points.
6. The multi-mode 5G broadcast transmitter adaptive predistortion method as claimed in claim 5, wherein the normalized least squares fitting error E of the adaptive algorithm module output signal is represented as:
wherein e (n) is the error between the output signal v (n) of the adaptive algorithm module and the original signal z (n).
7. The adaptive predistortion method for a multimode 5G broadcast transmitter according to claim 6, wherein the method for finding the suitable maximum memory depth under different working frequency bands and different signal bandwidths and the nonlinear order corresponding to each memory term under the maximum memory depth by calculating the normalized least square fitting error of the output signal of the adaptive algorithm module comprises:
step 1, initializing, and enabling P0=3, M =1, in which case the maximum memory depth M-1=0, the corresponding non-linear order is P0= 3; the output signal of the self-adaptive algorithm module is as follows:
v(n)=k0,0y(n)+k1,0y(n)|y(n)|1+k2,0y(n)|y(n)|2
calculating a normalized least squares fit error E (P)0)=E(3)；
Step 2, let PM-1=PM-1+1, that is, adding 1 to the non-linear order corresponding to the memory term with memory depth M-1, and calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-1)；
Step 3, if the E (P) obtained by calculation in step 20,P1,…,PM-1)≤α*E(P0,P1,…,PM-1-1), then jump to step 2; otherwise, let PM=PM1, deleting the non-linear order added in the step 2, and then jumping to the step 4; wherein alpha is a set error tolerance constant;
step 4, let M = M +1, PM-1=3, i.e. adding 1 memory term, and making the nonlinear order corresponding to the added memory term 3, then calculating the normalized least square fitting error E (P) at that time0,P1,…,PM-2,PM-1)=E(P0,P1,…,PM-2,3)；
Step 5, if the E (P) calculated in step 4 is obtained0,P1,…,PM-2,3)≤α*E(P0,P1,…,PM-2) If yes, jumping to the step 2; otherwise, making M = M-1, namely deleting the memory item added in the step 4, and ending the calculation process.
8. The adaptive predistortion method for a multimode 5G broadcast transmitter according to claim 7, wherein the value of the error tolerance constant α is in the range of [0.8,0.95 ].
9. An adaptive predistortion system of a multimode 5G broadcast transmitter, characterized in that the predistortion system adopts the adaptive predistortion method of the multimode 5G broadcast transmitter according to any one of claims 1 to 8 to realize the predistortion of a power amplifier.
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