CN101527544B - Device and method for identifying inverse characteristic of nonlinear system, power amplifier and predistorter thereof - Google Patents
Device and method for identifying inverse characteristic of nonlinear system, power amplifier and predistorter thereof Download PDFInfo
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- CN101527544B CN101527544B CN200810081674A CN200810081674A CN101527544B CN 101527544 B CN101527544 B CN 101527544B CN 200810081674 A CN200810081674 A CN 200810081674A CN 200810081674 A CN200810081674 A CN 200810081674A CN 101527544 B CN101527544 B CN 101527544B
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Abstract
The invention discloses a nonlinear system, in particular a device and a method for identifying inverse characteristic of a nonlinear power amplifier, a predistorter of the power amplifier and power amplification equipment using the device for identifying the inverse characteristic of the nonlinear system and the predistorter. The method for identifying the inverse characteristic of the nonlinearsystem according to an original input signal x(n) and a corresponding feedback output signal y<G>(n) of the nonlinear system comprises the following steps: according to an inverse filter signal x<F> (n) and the feedback output signal y<G>(n), calculating an inquiry form function Q[.]; according to the feedback output signal y<G>(n) and the inquiry form function Q[.], generating a middle predistortion signal z(n); according to the middle predistortion signal z(n) and the original input signal x(n), constructing a filter function F[.]; carrying out inverse filtering on the original input signalx(n) by using parameters of the filter function F[.] to generate an inverse filter signal x<F>(n); repeating each step through iteration till meeting the set condition, wherein the inquiry form function Q[.] and the filter function F[.] represent the inverse characteristic of the nonlinear system.
Description
Technical field
The nonlinear power amplifier that uses in the transmitter of relate generally to non linear system, particularly nonlinear power amplifier, especially wireless communication system of the present invention (such as base station and travelling carriage).More particularly; The present invention relates to the especially power amplification device of identifying inverse characteristic apparatus and method, power amplifier pre-distortion device and the use identifying inverse characteristic of nonlinear system device and the predistorter of nonlinear power amplifier of non linear system; It mainly utilizes digital technology to realize predistortion in base band, to remedy the memoryless nonlinear characteristic and the memory characteristic of non-linear benefit power amplifier.
Background technology
As typical case's representative of non linear system, power amplifier is the important component part of a lot of electronic equipments, and it can amplify the faint signal of telecommunication, to satisfy the needs of remote transmission.Wherein, the energy of amplification comes from DC power supply, and promptly power amplifier can be converted into AC signal with dc energy.
According to the physical characteristic of power amplifier, along with the power of input signal is ascending, the input signal of reflection power amplifier can be divided into linear zone, inelastic region and saturation region with the characteristic curve of the power relation of output signal.Fig. 1 illustrates the non-linear input/output signal characteristic of power amplifier PA.At input signal V
INThe zone that amplitude is less, the output V of power amplifier PA
OUTAlmost be input signal V
INLinear amplification, but along with input signal V
INThe increase of amplitude, the nonlinear characteristic of power amplifier PA are obviously to the last saturated gradually.This non-linear behavior is on frequency domain, because the intermodulation effect, video stretching appears outward in the band of the signal that is exaggerated, and then occurs distortion in the band, and is as shown in Figure 2, shown by the caused video stretching of nonlinear power amplifier.
Under the ideal situation, hope that power amplifier only plays the effect of linear amplification, promptly exporting signal is the simple linear amplification of input signal, therefore lets power amplifier be operated in linear zone.But this moment, power amplifier was very low with the efficient that direct current signal converts AC signal into, caused the waste of significant amount of energy, and needed to increase extra heat dissipation equipment.
Therefore; On the one hand in order to improve the efficient of power amplifier; On the other hand because the signal in a lot of Modern Communication System all has very big dynamic range (peak-to-average power ratio), so power amplifier often need the work in the inelastic region, thereby the distortion that causes exporting signal is (on frequency domain; The outer video stretching that then occurs of band distortion appears) in the band.In power amplifier field, only receive the distortion of the nonlinear power amplifier that current input influences to be called the memoryless nonlinear characteristic of power amplifier this current output usually.
In numerous methods that overcome the non-linearity of power amplifier characteristic, base band predistortion is a kind of method that receives much concern.As shown in Figure 3, the sketch map of the input/output signal characteristic of the power amplifier pre-distortion device that is used to offset nonlinear characteristic shown in Figure 1 is shown.The base band predistortion technology makes original input signal V through using the contrary characteristic of predistorter PD simulated power amplifier PA
INPredistortion takes place before the power amplifier being input to, thereby compensating power amplifier PA's is non-linear, and obtains not have the amplified output signals V that distorts at the output of power amplifier
OUTThe input/output signal characteristic of power amplifier with pre-distortion function is up near all showing extraordinary linear characteristic before the saturation region, and is as shown in Figure 4, and the sketch map of the input/output signal characteristic of the power amplifier that is provided with predistorter is shown.
But the good predistorter of great majority work all is directed against monotone signal or narrow band frequency signal.That is to say, mostly be used to compensate the memoryless non-linear of the top power amplifier of being mentioned.Yet; Along with the bandwidth of signal is more and more wideer; Power amplifier shows certain memory characteristic (frequency selectivity) again, and promptly current output signal is not only relevant with current input signal, and is also relevant with input signal before; As shown in Figure 5, the sketch map of the input/output signal characteristic of the nonlinear power amplifier with memory characteristic is shown.The memory characteristic of power amplifier shows as near the non-symmetrical spectrum the carrier wave on it is exported, as shown in Figure 6, and the spectrum diagram by the nonlinear power amplifier institute amplifying signal with memory characteristic is shown.That is to say that although carrier wave (desired signal) frequency spectrum is symmetrical fully, the pseudo-frequency spectrum that distortion caused but is asymmetric for centered carrier.
Therefore, in order to simulate the contrary characteristic of power amplifier exactly, come the contrary characteristic of power amplifier is carried out modeling and parameter Estimation with regard to adopting complicated more structure and method, so that realize predistortion with above-mentioned memory characteristic.Be used for the memoryless nonlinear characteristic of while compensating power amplifier and the technology of memory characteristic at present and mainly comprise memory multinomial model and two-dimensional polling list method.
The memory multinomial model is a Volterra simplified models model.The Volterra model adopts Volterra progression to come non linear system is carried out accurate modeling, can offset the memoryless nonlinear characteristic and the memory characteristic of power amplifier theoretically through the contrary Volterra progression model of emulation power amplifier.Though the Volterra model is very effective for the memoryless nonlinear characteristic and the memory characteristic of offsetting power amplifier, its physics realization almost is impossible, because need to use complicated formula, and needs the mathematical operation of flood tide.For this reason, the Volterra model of simplifying has been proposed, that is, and the memory multinomial model.As shown in Figure 7; Illustrate according to prior art and use the memory multinomial model that the input signal of nonlinear power amplifier with memory characteristic is carried out the sketch map of pre-distortion, wherein remember multinomial model and use with current input and the relevant polynomial function of previous input and represent to export.The nonlinear ability of offsetting power amplifier depends on the quantity and the polynomial exponent number of employed previous input.Describe the nonlinear characteristic of power amplifier subtly and need use the multinomial that contains higher order term, the generation that this often causes the matrix of big conditional number causes very difficulty of numerical computations.
The two-dimensional polling list method then is according to the relation between the current output of power amplifier and its current input and the last input; The contrary characteristic of power amplifier is processed question blank; Thereby before input signal being carried out power amplification, utilize the weight coefficient that from question blank, obtains that input signal is carried out predistortion, offset the nonlinear characteristic of power amplifier thus.As shown in Figure 8, illustrate according to prior art and use two-dimensional polling list the input signal of nonlinear power amplifier with memory characteristic to be carried out the sketch map of pre-distortion.But; Two-dimensional polling list not only needs very big internal memory to store a large amount of predistorting datas; And memory depth is limited, and the relation of often only utilizing current input and last input and output is carried out predistortion to the input signal of power amplifier, is used to offset the memory characteristic of power amplifier.Increase memory depth if desired, then the multi-dimensional query table will become very complicated, and be difficult to realize.
Also there is above similar techniques problem for other non linear system that is similar to nonlinear power amplifier.
Summary of the invention
In view of this, an object of the present invention is to provide a kind of identifying inverse characteristic of nonlinear system apparatus and method, can effectively reduce requirement memory device and numerical computations.
The power amplification device that another object of the present invention provides a kind of power amplifier identifying inverse characteristic apparatus and method, power amplifier pre-distortion device and uses power amplifier identifying inverse characteristic device and predistorter; It mainly utilizes digital technology to realize predistortion in base band; To remedy the memoryless nonlinear characteristic and the memory characteristic of power amplifier, effectively reduce requirement simultaneously to memory device and numerical computations.
According to an aspect of the present invention, a kind of identifying inverse characteristic of nonlinear system method is provided, is used for original input signal x (n) and corresponding feedback output signal y according to non linear system
G(n) the contrary characteristic of non linear system is carried out identification, comprising: question blank function Q { } generates step, according to liftering signal x
F(n) and feedback loop output signal y
G(n) calculate question blank function Q { }; Middle pre-distorted signals z (n) generates step, according to feedback loop output signal y
G(n) and question blank function Q { } generate in the middle of pre-distorted signals z (n); Filter function F{} constitution step is according to middle pre-distorted signals z (n) and original input signal x (n) structure filter function F{}; The liftering step, the parameter of utilizing filter function F{} is according to formula x
F(n)=F
-1{ x (n) } carries out liftering to original input signal x (n) and generates liftering signal x
F(n); And iteration repeats above each step till satisfying the condition of setting; Wherein, question blank function Q { } and filter function F{} represent the contrary characteristic of non linear system.
According to another aspect of the present invention, a kind of identifying inverse characteristic of nonlinear system device is provided, is used for original input signal x (n) and corresponding feedback output signal y according to non linear system
G(n) the contrary characteristic of non linear system is carried out identification, comprising: parameter calculator, be used for the contrary characteristic of non linear system is carried out identification, generate the question blank function Q { } and the filter function F{} of the contrary characteristic of expression non linear system; And the convergence decision device, be used for question blank function Q { } and filter function F{} that the critical parameter calculator generated and whether satisfy the condition of setting, and Control Parameter calculator iterative computation question blank function Q { } and filter function F{}; Wherein said parameter calculator comprises: the question blank maker is used for according to liftering signal x
F(n) and feedback loop output signal y
G(n) calculate question blank function Q { }; Middle predistorter is used for according to feedback loop output signal y
G(n) and the question blank function Q { } calculated of question blank maker generate in the middle of pre-distorted signals z (n); The filtering parameter calculator is used for the middle pre-distorted signals z (n) and original input signal x (n) the structure filter function F{} that generate according to middle predistorter; And inverse filter, be used to utilize filter function F{} that the filtering parameter calculator constructed according to formula x
F(n)=F
-1{ x (n) } carries out liftering to original input signal x (n) and generates liftering signal x
F(n).
According to a further aspect of the invention, a kind of power amplifier pre-distortion device is provided, comprises: address generator is used for original input signal is carried out being converted into the question blank address after delivery, the quantification; The one dimension question blank is configured according to question blank function Q { }, and according to the question blank address output calibration factor of address generator output; Multiplier is used for the correction factor of original input signal and the output of one dimension question blank being multiplied each other pre-distorted signals in the middle of obtaining; And filter, F{} is configured according to filter function, and the middle pre-distorted signals that multiplier is exported is carried out filtering, generates pre-distorted signals; Wherein, question blank function Q { } and filter function F{} generate according to identifying inverse characteristic of nonlinear system method recited above or generate according to identifying inverse characteristic of nonlinear system device recited above.
According to another aspect of the present invention, a kind of power amplification device is provided, comprises: predistortion module is used for original input signal is carried out predistortion the output predistorted input signal; Digital to analog converter is used for converting the predistorted input signal that predistortion module is exported into analog signal; Upconverter is used for the analog signal from digital to analog converter output is up-converted to radiofrequency signal; Power amplifier is used for the radiofrequency signal of upconverter output is carried out the output signal after power amplification and power output are amplified; Wherein, the structure of the power amplifier pre-distortion device that limited of the structure of said predistortion module and front is identical.
According to a further aspect of the invention; A kind of power amplification device is provided, comprises: predistortion module, its structure is with identical according to the structure of the power amplifier pre-distortion device that the front limited; Be used for original input signal is carried out predistortion the output predistorted input signal; Digital to analog converter is used for converting the predistorted input signal that predistortion module is exported into analog signal; Upconverter is used for the analog signal from digital to analog converter output is up-converted to radiofrequency signal; Power amplifier is used for the radiofrequency signal of upconverter output is carried out the output signal after power amplification and power output are amplified; Directional coupler is used for the output of power amplifier is branched into two paths of signals, and one road signal is exported as the output signal, and another road signal feedback is given attenuator; Attenuator is used to receive the signal that directional coupler feeds back, and this feedback signal is decayed; Low-converter is used for the deamplification of attenuator output is down-converted to baseband signal; Analog to digital converter is used for converting the baseband signal of low-converter output into digital signal, as feedback loop output signal; Delayer is used for original input signal is delayed time, and generates delay input signal; And predistorting data renovator; Be used to receive the delay input signal that feedback loop output signal that analog to digital converter exports and delayer are exported, and question blank function and filter coefficient carried out online updating according to feedback loop output signal that is received and delay input signal.
According to another aspect of the present invention, a kind of method of parameter of predistortion module of online updating power amplification device is provided, comprises step:
Relatively the original input signal x (n) and the feedback loop output signal of power amplification device, according to following formula calculation error signal:
Wherein, F{}, Q{}, P{} represent the filter function of filter, the question blank function of question blank and the magnification function of power amplifier respectively, and
z(n)＝x(n)·Q([|x(n)|])，
Question blank function Q { } does
Q([|x(n)|])＝[q
0，q
1，...，q
K-1]
T
Filter coefficient does
W＝[w
1，w
2，...，w
L-1]
T
L represents the memory depth of filter, and [||] represented delivery and quantification; And
According to following formula online updating question blank function and filter coefficient
Wherein
U＝[μ
1，μ
1，...，μ
L-1]
T
U and μ
qBe the convergence step-length.
According to the power amplification device of above-mentioned identifying inverse characteristic of nonlinear system apparatus and method of the present invention, power amplifier pre-distortion device and use power amplifier identifying inverse characteristic device and predistorter, avoided because the numerical computations difficulty that the high conditional number of matrix causes.
Provide other aspects of the present invention in the specification part below, wherein, detailed description is used for fully openly the preferred embodiments of the present invention, and it is not applied qualification.
Description of drawings
Below in conjunction with concrete embodiment, and, above and other objects of the present invention and advantage are done further description with reference to accompanying drawing.In the accompanying drawings, technical characterictic or parts identical or correspondence will adopt identical or corresponding Reference numeral to represent.
Fig. 1 is the sketch map of nonlinear characteristic that the input/output signal of power amplifier is shown;
Fig. 2 is the spectrum diagram that illustrates by nonlinear power amplifier institute amplifying signal;
Fig. 3 is the sketch map that the input/output signal characteristic of the power amplifier pre-distortion device that is used to offset nonlinear characteristic shown in Figure 1 is shown;
Fig. 4 is the sketch map that the input/output signal characteristic of the power amplifier that is provided with predistorter is shown;
Fig. 5 is the sketch map that the input/output signal characteristic of the nonlinear power amplifier with memory characteristic is shown;
Fig. 6 is the spectrum diagram that illustrates by the nonlinear power amplifier institute amplifying signal with memory characteristic;
Fig. 7 illustrates according to prior art to use the memory multinomial model input signal of nonlinear power amplifier with memory characteristic to be carried out the sketch map of pre-distortion;
Fig. 8 illustrates according to prior art to use two-dimensional polling list the input signal of nonlinear power amplifier with memory characteristic to be carried out the sketch map of pre-distortion;
Fig. 9 is the structural representation that the contrary characteristic of utilizing Hammerstein model construction non linear system is shown;
Figure 10 is the indirect learning ratio juris sketch map that obtains being used to of illustrating that the present invention adopts the contrary characteristic of non linear system;
Figure 11 illustrates the principle schematic of device for identifying that obtains the contrary characteristic of non linear system according to indirect learning method off-line of the present invention;
Figure 12 illustrates the flow chart of optimization process of discrimination method that obtains the contrary characteristic of non linear system according to indirect learning method off-line of the present invention;
Figure 13 is the block diagram that identifying inverse characteristic of nonlinear system device according to a preferred embodiment of the present invention is shown;
Figure 14 is the block diagram that the apparatus structure of the inputoutput data that is used to collect power amplifier is shown;
Figure 15 illustrates the block diagram that can realize the non-linear power multiplying arrangement of predistortion according to of the present invention;
Figure 16 be illustrate according to of the present invention can online updating the structure principle chart of power amplification device of parameter of upgrading predistortion module; And
Figure 17 is the block diagram that the exemplary configurations of personal computer is shown.
Embodiment
Embodiments of the invention are described with reference to the accompanying drawings.
The present invention for the identification of inverse characteristic of nonlinear system based on Hammerstein model commonly used.The Hammerstein model is to have considered the memory characteristic of non linear system and a kind of nonlinear organization model of setting up, is made up of memoryless nonlinear model and the series connection of linear time varying system two parts.Fig. 9 shows the structural representation that utilizes Hammerstein model 90 to make up the contrary characteristic of non linear system.As shown in Figure 9, Hammerstein model 90 comprise address generator 91, question blank (Look-Up Table, LUT) 92, FIR (finite impulse response) filter 93 and multiplier 95.Address generator 91 generates the address that is used for inquiring about LUT 92 according to input signal x (n), from LUT 92, selects the coefficient with complex representation according to this address then.With plural coefficient and input signal x (n) after multiplier 95 multiplies each other, resulting product carries out digital filtering through FIR filter 93, thereby forms final pre-distorted signals u (n).Here, the address of representing LUT 92 with k.
One dimension LUT can be used for the memoryless nonlinear characteristic of compensating non-linear system, and filter is used for the memory characteristic of compensating non-linear system, thereby has avoided taking the use of a large amount of internal memory and higher order polynomial, has reduced operand.Therefore, problem is converted into the parameter that how to make up this one dimension LUT and FIR filter, makes the two combination can fit the comprehensive nonlinear characteristic of non linear system preferably.For this reason, the present invention adopts " indirect learning method ".Figure 10 shows that the present invention adopts is used to obtain the indirect learning ratio juris of the contrary characteristic of non linear system.
Generally; The characteristic of non linear system can not change in time continually, so the module of learning algorithm and identification predistortion model A can be carried out the off-line data processing after collecting some input and output data samples (for example n input data sample and n dateout sampling point).After carrying out identification, new argument is sent to the predistortion model A module of non linear system, so that to comprehensive non-linear the compensating of non linear system.Shown in figure 10, this process is through when off-line Updates Information, and uses collected input and output data sample, obtains the contrary characteristic (parameter of estimation model A) of non linear system through minimization error signal e (n).In Figure 10, x (n) is the input of non linear system, and y (n) is the output of non linear system, and G is the expected gain coefficient of non linear system, and y
G(n) be called feedback nonlinear systems output.Predistortion model A module can be used various programming devices realizations such as field programmable gate array (FPGA), thereby easily realizes the on-the-spot time-varying parameter that upgrades non linear system etc.
Figure 11 illustrates the device for identifying that obtains the contrary characteristic of non linear system according to indirect learning method off-line, the principle schematic of the module of learning algorithm promptly shown in Figure 10 and identification predistortion model A.The principle of this device is based on indirect learning method shown in Figure 10.Data collection memory module 301 among the figure will be collected the inputoutput data of the non linear system of storage and given recognition module 300; 300 input data with the non linear system of these data map output backs and 301 storages of data collection memory module of recognition module compare through adder 306, and revise the foundation of parameter in the recognition module 300 with error signal e (n) conduct.
Can know that according to Figure 11 error signal e (n) can be expressed as
Wherein x (n) representes input signal shown in Figure 10.Expected gain in non linear system shown in Figure 10 is G, and non linear system is output as under the situation of y (n), y
G(n) can represent with following formula
y
G(n)＝y(n)/G (2)
Middle pre-distorted signals z (n) does
z(n)＝y
G(n)·Q([|y
G(n)|]) (4)
Here, F{} represents the filter function of filter 305, and Q{} represents the question blank function of question blank 303.
[||] represented address generator 302 performed delivery and quantification, promptly
k＝[|y
G(n)|] k＝0，...，K-1 (5)
An address in the middle of the corresponding question blank of k (for example 0～255).
The problem of thus, the contrary characteristic of non linear system being carried out identification is converted into through the designing filter 305 and the parameter of question blank 303 comes minimization error signal e (n).Consider that e (n) is a time series, this identification problem can be write:
Here
For minimization function E effectively, best bet is combined optimization F{} and Q{}.Present embodiment proposes a kind of computational methods, comes minimization E through computation optimization F{} alternately with Q{}, the contrary characteristic that F{} that obtains with optimization and Q{} (filter 305 and question blank 303) approach non linear system.
At first come analytical calculation function Q { }.The content of question blank can obtain through the gain of analyzing each sampling point, and the gain of each sampling point here can be expressed as
gp(n)＝x(n)/y
G(n) (8)
That is to say the corresponding y of each gp (n)
G(n) the address k in the middle of the also just corresponding question blank of y, the gp (n) that will have identical address k value is classified as one type (putting a group under), and its note is done
GP
k＝[gp(i)，...gp(j)] 0＜k＜K-1，
1＜i，j＜N?(9)
Gp (n) in each group is made even all to mould value and phase place respectively, the gain characteristic that can obtain to trade off, promptly
g
k＝mean(|GP
k|) (10)
p
k＝mean(∠GP
k) (11)
So, can obtain question blank function Q { }
Q{·}＝Gain⊙exp(j·Phase) (12)
Wherein
Gain＝[g
0，...，g
i，...]
T (13)
Phase＝[p
0，...p
i，...]
T (14)
And ⊙ representes the Hadamard product, and subscript T representes matrix transpose.
After obtaining question blank function Q { } according to formula (12), pre-distorted signals z (n) in the middle of can calculating according to formula (4).
Should be noted that, when calculating compromise gain characteristic in the above, selection be mould value and phase place mean value separately of gp (n) in each group.Can certainly use other to well known to a person skilled in the art that algorithm obtains this compromise gain characteristic, such as median method, least square method or the like, omits concrete associated description at this.
Following analytical calculation filter function F{}, this can obtain through least square method.In the middle of obtaining, behind the pre-distorted signals z (n),, can obtain filter coefficient according to least square method in order to obtain optimum filter function F{} to approach input signal x (n):
W＝(Z
M HZ
M)
-1·Z
M H·X (15)
Wherein
X＝[x(1)，x(2)，...，x(N)]
T (16)
Here, N is the length of the data of collection, and L is the exponent number (memory depth) of FIR filter.
After calculating the parameter W of filter according to formula (15), filter function F{} can be expressed as with matrix and vector form:
F{z(n)}＝Z
M·W (18)
Then, according to formula (3) and formula (18) can obtain input signal x (n) estimated value
thus according to formula (1) error signal e (n).If error signal e (n) satisfies the condition of setting, think that then the estimated value
of input signal x (n) meets the demands.This means that presently used question blank function Q { } and filter function F{} have fitted the characteristic of non linear system well, can be as the parameter of predistortion model A shown in Figure 10.
On the other hand; If error signal e (n) does not satisfy the condition of setting; Then the estimated value of input signal x (n)
can not meet the demands, and need further revise question blank function Q { } and filter function F{}.At this moment, utilize 305 couples of input signal x of filter (n) to carry out liftering and obtain liftering signal x according to following formula
F(n):
x
F(n)＝F
-1{x(n)} (19)
Then, use this liftering signal x
F(n) replace the original input signal x (n) in the formula (8) to export signal y with feedback nonlinear systems
G(n) together, utilize formula (4), (5) and formula (12) to formula (18) to recomputate question blank function Q { } and filter function F{}.Then, again according to formula (1) error signal e (n), till satisfying the condition of setting.
Here, the condition of setting can be for after obtaining error signal e (n), and whether the standard mean square error NMSE shown in the formula (20) below checking restrains or judge that whether iterations is greater than certain threshold value.
Certainly, it should be appreciated by those skilled in the art that the standard mean square error NMSE that the condition of setting is not limited only to here to be mentioned also can be other criterion, such as mean square error MSE, peak value mean square error PMSE or the like.
Figure 12 shows the flow chart of the optimization process of being carried out by recognition module shown in Figure 11 300, promptly how according to the input signal x (n) of non linear system with export question blank 303 function Q { } and the function F { } of filter 305 that signal y (n) obtains to be used to characterize the contrary characteristic of non linear system.
Shown in figure 12, at first obtain the original input signal x (n) of non linear system at step S1201, and through non linear system handle, then through the feedback and the resulting output signal y that handles by recognition module 300 that decays
G(n).
Then, will export signal y at step S1202
G(n) sampling point ground carries out delivery and quantization operation one by one, according to the output signal y of formula (5) acquisition with each sampling point
G(n) corresponding address numbering k.Afterwards, at step S1203, set primary standard mean square error NMSE
0, and make iteration variable i=0.
Then, divide into groups to obtain question blank function Q { } according to formula (8)～(14) at step S1204 with average.At first, obtain pointwise gain gp (n) according to formula (8), and according to formula (9) one by one the sampling point gain be divided into some groups according to the output Signal Message Address of correspondence.Then, obtain question blank function Q { } according to (10)～(14).
Calculating acquisition question blank function Q { } afterwards,, calculate middle pre-distorted signals z (n) according to formula (4) at step S1205.Afterwards, couple iteration variable i increases progressively 1 in step 1206, i.e. i=i+1.
Then, carry out Design of Filter at step S1206 and obtain filter function F{}, and basis of calculation mean square error NMSE
iAt first, according to formula (17) structure filtering matrix Z
MAnd according to formula (15) acquisition filter function F{}.Calculate the estimated value of input signal x (n) then according to formula (3) and formula (18)
And according to formula (20) basis of calculation mean square error NMSE
i
At the standard of acquisition mean square error NMSE
iAfterwards, judge whether standard mean square error NMSE at step S1208
iWhether convergence or iterations be greater than certain threshold value.If then handling process advances to step S1209, current question blank function Q { } and filter function F{} are exported to on-site programmable gate array FPGA, so that realize predistorter from hardware.
If the judged result at step S1208 shows standard mean square error NMSE
iDo not restrain and iterations is not more than certain threshold value, then handling process advances to step S1210, utilizes filter that input signal x (n) is carried out liftering and obtains x
F(n)=F
-1{ x (n) }.
Then, handling process turns back to step S1204, iteration execution in step S1204～S1208.Use this liftering signal x
F(n) replace the original input signal x (n) in the formula (8) to export signal y with feedback nonlinear systems
G(n) together, utilize formula (4), (5) and formula (12) to formula (18) to recomputate question blank function Q { } and filter function F{}.Then, again according to formula (20) basis of calculation mean square error NMSE
i, up to standard mean square error NMSE
iThe convergence or iterations greater than certain threshold value till.
To describe the structure of the preferred identifying inverse characteristic of nonlinear system device of realizing above-mentioned discrimination method below in detail.Shown in figure 13, the block diagram of identifying inverse characteristic of nonlinear system device according to a preferred embodiment of the present invention is shown.
In data storage device shown in Figure 13 810, store original input signal x (n) and through the feedback loop output signal y of overdamping
G(n), these signals are fed to parameter calculator 800.Parameter calculator 800 comprises unit such as address generator 801, question blank maker 802, filtering parameter calculator 803, middle predistorter 804, filter 805, inverse filter 806 and selector 807.
Feedback loop output signal y
G(n) behind the input parameter calculator 800, convert address k into according to formula (5) by address generator 801.Address k that is changed and feedback loop output signal y
G(n) be imported into question blank maker 802 together.In addition, question blank maker 802 is also imported signal x (n) or the liftering signal x from selector 807
F(n), and according to formula (8)～(14) generated query table function Q{}, the question blank function Q { } that is generated is outputed in the middle of predistorter 804.
Question blank function Q { } and feedback loop output signal y that middle predistorter 804 receives from question blank maker 802
G(n), thereby generate middle pre-distorted signals z (n), and the middle pre-distorted signals z (n) that is generated is outputed to filtering parameter calculator 803 and filter 805 respectively according to formula (4).
Middle pre-distorted signals z (n) and original input signal x (n) that filtering parameter calculator 803 receives from middle predistorter 804, and according to formula (15)～(18) generation filter function F{}.The filter function F{} that is generated outputs to filter 805 and inverse filter 806 respectively.
The filter function F{} that filter 805 is generated according to filtering parameter calculator 803, the middle pre-distorted signals z (n) that middle predistorter 804 is generated according to formula (3) handle the estimated value
that generates original input signal and the estimated value
of the original input signal that is generated are outputed to NMSE calculator 808.
The estimated value
and the original input signal x (n) of the original input signal that NMSE calculator 808 receiving filters 805 are generated, and calculate outputting standard mean square error NMSE according to formula (20) and arrive convergence decision device 811.
Also receive filter function F{} with filter 805 symmetrically arranged inverse filters 806, and use this filter function F{} that original input signal x (n) is carried out liftering, output liftering signal x from filtering parameter calculator 803
F(n) to selector 807.
Convergence decision device 811 record standard mean square error NMSE and iteration step number, and with standard mean square error NMSE or iteration step number as decision condition.If do not satisfy decision condition, that is, if standard mean square error NMSE not convergence or iteration step number be no more than predetermined threshold, then restrain the decision device 811 Control Parameter calculators 800 execution said process that iterates.If satisfy decision condition, that is,, then restrain filter function F{} and question blank function Q { } that decision device 811 Control Parameter calculators 800 output latest computed obtain if standard mean square error NMSE convergence or iteration step number have surpassed predetermined threshold.
To be that example specifies and how to utilize identifying inverse characteristic of nonlinear system method recited above and device for identifying to come the memoryless nonlinear characteristic and the memory characteristic of power amplifier are compensated below with the nonlinear power amplifier, thereby expand its linear amplification district.That is to say how to utilize identifying inverse characteristic of nonlinear system method recited above and device for identifying to come nonlinear power amplifier is carried out predistortion.
At first need collect, so that its contrary characteristic is carried out identification the inputoutput data of nonlinear power amplifier.Figure 14 shows the block diagram of the apparatus structure of the inputoutput data that is used to collect power amplifier.It is pointed out that the power amplifier here is is that example describes with the power amplifier that the transmitter of base station in the communication system and travelling carriage is used always, this power amplifier also can be applied in other occasion certainly.
Shown in figure 14, this device that is used to collect the inputoutput data of power amplifier comprises: digital to analog converter 101 is used for converting digital input signals x (n) into analog signal (for baseband signal); Upconverter 102 is used for the baseband signal from digital to analog converter 101 outputs is up-converted to radiofrequency signal; Power amplifier 103 is used for the radiofrequency signal of upconverter 102 outputs is carried out power amplification; Directional coupler 104 is used for the output of power amplifier 103 is branched into two paths of signals, and one road signal is exported as output signal y (n), and another road signal feedback is given attenuator 105; Attenuator 105 is used to receive the output of the power amplifier 103 of directional coupler 104 feedbacks, and this feedback signal is decayed; Low-converter 106 is used for the deamplification of attenuator 105 outputs is down-converted to baseband signal; Analog to digital converter 107 is used for that low-converter 106 is carried out the resulting baseband signal of down-conversion and converts digital signal into, that is, and and feedback loop output signal y
G(n); And data collection memory module 100, be used for collecting storage original input signal x (n) and feedback loop output signal y
G(n).The original input signal x (n) and the feedback loop output signal y that are stored in the data collection memory module 100
G(n) represent as follows with vector form:
X＝[x(1)，x(2)，......，x(n)] (21)
Y
G＝[y
G(1)，y
G(2)，......，y
G(n)] (22)
The original input signal x (n) and the feedback loop output signal y of power amplifier have been collected at use device shown in Figure 14
G(n) afterwards, can use identifying inverse characteristic of nonlinear system method shown in Figure 12 or identifying inverse characteristic of nonlinear system device shown in Figure 13 that the contrary characteristic of this power amplifier is carried out identification, thereby obtain filter function F{} and question blank function Q { }.
Obtaining filter function F{} and question blank function Q { } afterwards, just can make up the predistortion module of power amplifier and be used for the input signal x (n) of power amplifier is carried out predistortion, thereby power amplifier acquisition favorable linearity is exported.Figure 15 shows the block diagram that can realize the non-linear power multiplying arrangement of predistortion according to embodiments of the invention.
Shown in figure 15, this non-linear power multiplying arrangement that can realize predistortion comprises: predistortion module 400 is used for input signal x (n) is carried out predistortion the output pre-distorted signals; Digital to analog converter 405, the pre-distorted signals that is used for that predistortion module 400 is exported convert analog signal (for baseband signal) into; Upconverter 406 is used for the baseband signal from digital to analog converter 405 outputs is up-converted to radiofrequency signal; Power amplifier 407 is used for the radiofrequency signal of upconverter 406 output is carried out the output signal y (n) after power amplification and power output are amplified.At this moment, the signal y (n) that power amplification device is exported has eliminated the memoryless nonlinear characteristic and the memory characteristic of power amplifier, has the favorable linearity characteristic.
In addition; Also can directional coupler 104, attenuator 105, low-converter 106, analog to digital converter 107 and the data collection memory module 100 of device that the inputoutput data of power amplifier is collected in shown in Figure 14 being used for be attached in the power amplification device shown in Figure 15, so that constitute power amplification device with data collection memory function and predistortion function.
According to the non-linear power multiplying arrangement that as above disposes with predistortion module; Utilize digital technology to realize predistortion in base band; Not only can remedy the memoryless nonlinear characteristic and the memory characteristic of power amplifier, and can effectively reduce the capacity requirement of memory device and greatly reduce the requirement that logarithm value is calculated.
Above described be to utilize input signal x (n) and the feedback loop output signal y that collects the power amplifier of storage in advance
G(n) the contrary characteristic of power amplifier is carried out off-line identification, then according to the predistortion module of identification result allocating power amplifier.
But as what in preamble, mentioned, nonlinear power amplifier is as a kind of non linear system, and its output characteristic can change along with change of time.Though not remarkable in this variation short time, along with its characteristic of prolongation of time must change, thereby preferably regularly the contrary characteristic of power amplifier is carried out identification, so that being exported, it always shows the favorable linearity form.
For fear of the loaded down with trivial details work that the parameter of the contrary characteristic of regular identification power amplifier and its predistortion module of regular update is brought, present embodiment also proposes a kind of power-magnifying method and equipment of parameter of online updating upgrading predistortion module.Shown in figure 16, show according to of the present invention can online updating the structure principle chart of power amplification device of parameter of upgrading predistortion module.
Power amplification device shown in Figure 16 comprises predistortion module 400, digital to analog converter 405, upconverter 406, power amplifier 407, directional coupler 104, attenuator 105, low-converter 106, analog to digital converter 107, predistorting data renovator 600 and delayer 601.Except predistorting data renovator 600 with the delayer 601, remaining each unit structure with Figure 14 and corresponding units shown in Figure 15 respectively is identical, and representes with identical Reference numeral, in this its detailed description of omission.
In the power amplification device with online updating predistortion module shown in Figure 16, at first original figure input signal x (n) input predistortion module 400 converts the question blank address into after address generator 401 deliverys, quantification.From one dimension question blank 402, read correction factor according to this question blank address, and this correction factor and original figure input signal x (n) are multiplied each other in multiplier 403.Result after multiplying each other carries out filtering through filter 404 again and accomplishes pre-distortion, generates pre-distorted signals.Then, pre-distorted signals becomes radio frequency analog signal through digital to analog converter 405 and upconverter 406, sends to the signal y (n) after power amplifier 407 carries out power amplification and output amplification.
Output signal y (n) after the amplification feeds back through directional coupler 104; Then through attenuator 105 decay; Low-converter 106 down-converts the signals to base band afterwards, converts baseband signal into digital signal by A-D converter 107 again, and is input in the predistorting data renovator 600.
Original digital input signals x (n) then has one the tunnel through also being input in the predistorting data renovator 600 after delayer 601 time-delays.Predistorting data renovator 600 is equivalent to recognition module shown in Figure 11 300, and upgrades filter function F{} and question blank function Q { } in the predistortion module 400 according to following algorithm.
From Figure 16, can derive, original digital input signals x (n) is compared with the digital output signal that feeds back can obtain error signal:
Wherein, P{} represents the power amplifier function, and
z(n)＝x(n)·Q([|x(n)|]) (24)
Question blank function Q { } can be write
Q([|x(n)|])＝[q
0，q
1，...，q
K-1]
T (25)
Filter coefficient can be put in order and do
W＝[w
1，w
2，...，w
L-1]
T (26)
L still represents memory depth.
So question blank function and filter coefficient can upgrade according to following formula:
Wherein
U＝[μ
1，μ
1，...，μ
L-1]
T (28)
U and μ
qIt all is the convergence step-length.
Certainly, predistorting data renovator 600 also can utilize up-to-date original input signal x (n) and the feedback loop output signal y that obtains according to foregoing nonlinear inverse characteristic discrimination method
G(n) the contrary characteristic of power amplifier is carried out on-line identification, to obtain real-time question blank function Q { } and filter function F{}.
After having obtained new question blank function and filter function; Just can make amendment, omit detailed description by the flow process of the performed processing method of predistortion module shown in Figure 16 400 and predistorting data renovator 600 at this to the relevant parameter in the predistortion module 400.
According to disclosed device for identifying of present embodiment and discrimination method, not only can reduce requirement simultaneously, and can realize the online updating of predistortion module parameter memory device and numerical computations, thereby real-time ensuring the linear characteristic of power amplifier.
Above with nonlinear power amplifier how to describe in detail as an example to the time become non linear system and carry out off-line and online identifying inverse characteristic, and non linear system is carried out predistortion according to identification result.
As stated; The present invention adopts an one dimension question blank and finite impulse response (FIR) filter to connect and describes the nonlinear characteristic of power amplifier; Thereby avoided the nonlinear characteristic that employed a lot of higher order terms accurately describe power amplifier in the using memory multinomial model; Because the use of higher order term often causes the conditional number of matrix correspondingly very high, give and accurately obtain the memory polynomial parameters and predistortion has been brought difficulty.And, according to power amplifier pre-distortion device of the present invention and use power amplifier identifying inverse characteristic device and the power amplification device of predistorter adopts is the one dimension question blank, saved the internal memory needs greatly.
In addition, should also be noted that above-mentioned series of processes and device also can be through software and/or firmware realizations.Under situation about realizing through software and/or firmware; From storage medium or network to computer with specialized hardware structure; General purpose personal computer 700 for example shown in Figure 17 is installed the program that constitutes this software, and this computer can be carried out various functions or the like when various program is installed.
In Figure 17, CPU (CPU) 701 carries out various processing according to program stored among read-only memory (ROM) 702 or from the program that storage area 708 is loaded into random-access memory (ram) 703.In RAM 703, also store data required when CPU 701 carries out various processing or the like as required.
Following parts are connected to input/output interface 705: importation 706 comprises keyboard, mouse or the like; Output 707 comprises display, such as cathode ray tube (CRT), LCD (LCD) or the like and loud speaker or the like; Storage area 708 comprises hard disk or the like; With communications portion 709, comprise that NIC is such as LAN card, modulator-demodulator or the like.Communications portion 709 is handled such as the internet executive communication via network.
As required, driver 710 also is connected to input/output interface 705.Detachable media 711 is installed on the driver 710 such as disk, CD, magneto optical disk, semiconductor memory or the like as required, makes the computer program of therefrom reading be installed to as required in the storage area 708.
Realizing through software under the situation of above-mentioned series of processes, such as detachable media 711 program that constitutes software is being installed such as internet or storage medium from network.
It will be understood by those of skill in the art that this storage medium is not limited to shown in Figure 17 wherein having program stored therein, distribute so that the detachable media 711 of program to be provided to the user with equipment with being separated.The example of detachable media 711 comprises disk (comprising floppy disk (registered trade mark)), CD (comprising compact disc read-only memory (CD-ROM) and digital universal disc (DVD)), magneto optical disk (comprising mini-disk (MD) (registered trade mark)) and semiconductor memory.Perhaps, storage medium can be hard disk that comprises in ROM 702, the storage area 708 or the like, computer program stored wherein, and be distributed to the user with the equipment that comprises them.
The step that also it is pointed out that the above-mentioned series of processes of execution can order following the instructions naturally be carried out in chronological order, but does not need necessarily to carry out according to time sequencing.Some step can walk abreast or carry out independently of one another.
Though specified the present invention and advantage thereof, be to be understood that and under not breaking away from, can carry out various changes, alternative and conversion the situation of the appended the spirit and scope of the present invention that claim limited.And; The application's term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability; Thereby make and comprise that process, method, article or the equipment of a series of key elements not only comprise those key elements; But also comprise other key elements of clearly not listing, or also be included as this process, method, article or equipment intrinsic key element.Under the situation that do not having much more more restrictions, the key element that limits by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises said key element and also have other identical element.
Claims (29)
1. an identifying inverse characteristic of nonlinear system method is used for original input signal x (n) and corresponding feedback output signal y according to non linear system
G(n) the contrary characteristic of non linear system is carried out identification, comprising:
Question blank function Q { } generates step, according to liftering signal x
F(n) and feedback loop output signal y
G(n) calculate question blank function Q { };
Middle pre-distorted signals z (n) generates step, according to feedback loop output signal y
G(n) and question blank function Q { } generate in the middle of pre-distorted signals z (n);
Filter function F{} constitution step is according to middle pre-distorted signals z (n) and original input signal x (n) structure filter function F{};
The liftering step utilizes filter function F{} according to formula x
F(n)=F
-1{ x (n) } carries out liftering to original input signal x (n) and generates liftering signal x
F(n); And
Iteration repeats above each step till satisfying the condition of setting;
Wherein, question blank function Q { } and filter function F{} represent the contrary characteristic of non linear system;
Wherein, the condition of said setting is to confirm standard mean square error NMSE convergence shown in the following formula or iterations greater than predetermined threshold,
Formula
according to the following formula using the filter function F {·} and the intermediate pre-distortion signal z (n) to generate the original input signal x (n) the estimated value of
2. identifying inverse characteristic of nonlinear system method according to claim 1, wherein in first time during generated query table function Q{}, with original input signal x (n) as liftering signal x
F(n) with feedback loop output signal y
G(n) calculate question blank function Q { } together.
3. identifying inverse characteristic of nonlinear system method according to claim 1 also comprises delivery and quantization step, according to following formula with feedback loop output signal y
G(n) be quantified as some address k in the question blank,
k＝[|y
G(n)|] k＝0，...，K-1
Wherein, [||] represented delivery and quantification.
4. identifying inverse characteristic of nonlinear system method according to claim 3, wherein question blank function Q { } generation step comprises:
According to formula gp (n)=x
F(n)/y
G(n) calculate the gain of each sampling point;
The gp (n) that will have identical address is divided into a group according to following formula,
GP
k＝[gp(i)，...gp(j)] 0＜k＜K-1，
1＜i，j＜N
So that it is corresponding with an address k in the question blank; And
Obtain the gain of each group, thus generated query table function Q{}.
5. identifying inverse characteristic of nonlinear system method according to claim 4, the step that wherein obtains the gain of each group comprises:
Gp (n) in each group is made even all to mould value and phase place respectively according to following formula,
g
k＝mean(|GP
k|)
p
k＝mean(∠GP
k)
Thereby obtain question blank function Q { }
Q{·}＝Gain?⊙exp(j·Phase)
Wherein
Gain＝[g
0，...，g
i，...]
T
Phase＝[p
0，...p
i，...]
T
⊙ representes the Hadamard product, and subscript T representes matrix transpose.
6. identifying inverse characteristic of nonlinear system method according to claim 5 wherein generates in the step at middle pre-distorted signals z (n), uses feedback loop output signal y according to following formula
G(n) and question blank function Q { } generate in the middle of pre-distorted signals z (n):
z(n)＝y
G(n)·Q([|y
G(n)|])。
7. identifying inverse characteristic of nonlinear system method according to claim 6 wherein in filter function F{} constitution step, is used middle pre-distorted signals z (n) and original input signal x (n) structure filter factor according to following formula
W＝(Z
M HZ
M)
-1·Z
M H·X
Wherein
X＝[x(1)，x(2)，...，x(N)]
T
N is the length of the data of collection, and L is the exponent number of filtering, thereby filter function F{} is expressed as with matrix and vector form:
F{z(n)}＝Z
M·W。
8. identifying inverse characteristic of nonlinear system method according to claim 7 is wherein exported the contrary characteristic as non linear system with question blank function Q { } and filter function F{} when satisfying the condition of setting.
9. according to the arbitrary described identifying inverse characteristic of nonlinear system method of claim 1～8, wherein feedback loop output signal y
G(n) generate according to following formula
y
G(n)＝y(n)/G
G is the expected gain of non linear system, and y (n) is the output of non linear system.
10. identifying inverse characteristic of nonlinear system method according to claim 9 also comprises data collection step, is used for non linear system is applied specific original input signal x (n) and obtains corresponding output signal y (n).
11. an identifying inverse characteristic of nonlinear system device is used for original input signal x (n) and corresponding feedback output signal y according to non linear system
G(n) the contrary characteristic of non linear system is carried out identification, comprising:
Parameter calculator is used for the contrary characteristic of non linear system is carried out identification, generates the question blank function Q { } and the filter function F{} of the contrary characteristic of expression non linear system; And
The convergence decision device is used for the condition whether question blank function Q { } that the critical parameter calculator generated and filter function F{} satisfy setting, and Control Parameter calculator iterative computation question blank function Q { } and filter function F{};
Wherein, said parameter calculator comprises:
The question blank maker is used for according to liftering signal x
F(n) and feedback loop output signal y
G(n) calculate question blank function Q { };
Middle predistorter is used for according to feedback loop output signal y
G(n) and the question blank function Q { } calculated of question blank maker generate in the middle of pre-distorted signals z (n);
The filtering parameter calculator is used for the middle pre-distorted signals z (n) and original input signal x (n) the structure filter function F{} that generate according to middle predistorter; And
Inverse filter is used to utilize filter function F{} that the filtering parameter calculator constructed according to formula x
F(n)=F
-1{ x (n) } carries out liftering to original input signal x (n) and generates liftering signal x
F(n).
12. identifying inverse characteristic of nonlinear system device according to claim 11 also comprises selector, is used for selecting original input signal x (n) as liftering signal x in first time during generated query table function Q{}
F(n) be input to the question blank maker, make the question blank maker according to original input signal x (n) and feedback loop output signal y
G(n) calculate question blank function Q { }, and after during generated query table function Q{}, select the liftering signal x that inverse filter generated
F(n) be input to the question blank maker, make the question blank maker according to liftering signal x
F(n) and feedback loop output signal y
G(n) calculate question blank function Q { }.
13. identifying inverse characteristic of nonlinear system device according to claim 12; Also comprise: filter is used for the estimated value
of using filter function F{} that the filtering parameter calculator constructed and middle pre-distorted signals z (n) to generate original input signal x (n) according to formula
Standard mean square error NMSE calculator is used for according to following formula basis of calculation mean square error NMSE
Wherein, restrain whether decision device restrains according to standard mean square error NMSE or whether iterations judges whether satisfy the condition of setting greater than predetermined threshold.
14. identifying inverse characteristic of nonlinear system device according to claim 13 also comprises address generator, is used for according to following formula feedback loop output signal y
G(n) be quantified as some address k in the question blank that the question blank maker generated,
k＝[|y
G(n)|] k＝0，...，K-1
Wherein, [||] represented delivery and quantification.
15. identifying inverse characteristic of nonlinear system device according to claim 14, wherein the question blank maker is configured to
According to formula gp (n)=x
F(n)/y
G(n) calculate the gain of each sampling point;
The gp (n) that will have identical address is divided into a group according to following formula,
GP
k＝[gp(i)，...gp(j)] 0＜k＜K-1，
1＜i，j＜N
So that it is corresponding with an address k in the question blank; And
Obtain the gain of each group, thus generated query table function Q{}.
16. identifying inverse characteristic of nonlinear system device according to claim 15, wherein the question blank maker is configured to
Gp (n) in each group is made even all to mould value and phase place respectively according to following formula,
g
k＝mean(|GP
k|)
p
k＝mean(∠GP
k)
Thereby obtain question blank function Q { }
Q{·}＝Gain?⊙exp(j.Phase)
Wherein
Gain＝[g?
0，...，g
i，...]
T
Phase＝[p
0，...p
i，...]
T
⊙ representes the Hadamard product, and subscript T representes matrix transpose.
17. identifying inverse characteristic of nonlinear system device according to claim 16, wherein middle predistorter uses feedback loop output signal y according to following formula
G(n) and the question blank function Q { } calculated of question blank maker generate in the middle of pre-distorted signals z (n):
z(n)＝y
G(n)·Q([|y
G(n)|])。
18. identifying inverse characteristic of nonlinear system device according to claim 17, predistorter was generated in the middle of wherein the filtering parameter calculator used according to following formula middle pre-distorted signals z (n) and original input signal x (n) structure filter coefficient
W＝(Z
M HZ
M)
-1·Z
M H·X
Wherein
X＝[x(1)，x(2)，...，x(N)]
T
N is the length of the data of collection, and L is the exponent number of filtering, thereby filter function F{} is expressed as with matrix and vector form:
F{z(n)}＝Z
M·W。
19. identifying inverse characteristic of nonlinear system device according to claim 18 is wherein exported the contrary characteristic as non linear system with question blank function Q { } and filter function F{} when satisfying the condition of setting.
20. according to the arbitrary described identifying inverse characteristic of nonlinear system device of claim 11～19, wherein feedback loop output signal y
G(n) generate according to following formula
y
G(n)＝y(n)/G
G is the expected gain of non linear system, and y (n) is the output of non linear system.
21. identifying inverse characteristic of nonlinear system device according to claim 20 also comprises the data collection memory device, is used for non linear system is applied specific input signal x (n) and obtains corresponding output signal y (n).
22. a power amplifier pre-distortion device comprises:
Address generator is used for original input signal is carried out being converted into the question blank address after delivery, the quantification;
The one dimension question blank is configured according to question blank function Q { }, and according to the question blank address output calibration factor of address generator output;
Multiplier is used for the correction factor of original input signal and the output of one dimension question blank being multiplied each other pre-distorted signals in the middle of obtaining; And
Filter, F{} is configured according to filter function, and the middle pre-distorted signals that multiplier is exported is carried out filtering, generates pre-distorted signals;
Wherein, question blank function Q { } and filter function F{} generate according to the arbitrary described identifying inverse characteristic of nonlinear system method of claim 1～8 or generate according to the arbitrary described identifying inverse characteristic of nonlinear system device of claim 11～19.
23. power amplifier pre-distortion device according to claim 22 also comprises the data collection storage device, is used to collect the original input signal and the feedback loop output signal of power amplifier, this data collection storage device comprises:
Digital to analog converter is used for converting original input signal into analog signal;
Upconverter is used for the analog signal from digital to analog converter output is up-converted to radiofrequency signal;
Power amplifier is used for the radiofrequency signal of upconverter output is carried out power amplification;
Directional coupler is used for the output of power amplifier is branched into two paths of signals, and one road signal is exported as the output signal, and another road signal feedback is given attenuator;
Attenuator is used to receive the signal that directional coupler feeds back, and this feedback signal is decayed;
Low-converter is used for the deamplification of attenuator output is down-converted to baseband signal;
Analog to digital converter is used for converting the baseband signal of low-converter output into digital signal, as feedback loop output signal; And
The data collection memory module is used for collecting storage original input signal and feedback loop output signal.
24. a power amplification device comprises:
Predistortion module is used for original input signal is carried out predistortion, the output predistorted input signal;
Digital to analog converter is used for converting the predistorted input signal that predistortion module is exported into analog signal;
Upconverter is used for the analog signal from digital to analog converter output is up-converted to radiofrequency signal;
Power amplifier is used for the radiofrequency signal of upconverter output is carried out signal after power amplification and power output are amplified as the output signal;
Wherein, the structure of said predistortion module is identical with the structure of power amplifier pre-distortion device according to claim 22.
25. power amplification device according to claim 24 also comprises:
Directional coupler is used for the output of power amplifier is branched into two paths of signals, and one road signal is exported as the output signal, and another road signal feedback is given attenuator;
Attenuator is used to receive the signal that directional coupler feeds back, and this feedback signal is decayed;
Low-converter is used for the deamplification of attenuator output is down-converted to baseband signal;
Analog to digital converter is used for converting the baseband signal of low-converter output into digital signal, as feedback loop output signal; And
The data collection memory module is used for collecting storage original input signal and feedback loop output signal.
26. a power amplification device comprises:
Predistortion module, its structure is identical with the structure of power amplifier pre-distortion device according to claim 22, is used for original input signal is carried out predistortion the output predistorted input signal;
Digital to analog converter is used for converting the predistorted input signal that predistortion module is exported into analog signal;
Upconverter is used for the analog signal from digital to analog converter output is up-converted to radiofrequency signal;
Power amplifier is used for the radiofrequency signal of upconverter output is carried out signal after power amplification and power output are amplified as the output signal;
Directional coupler is used for the output of power amplifier is branched into two paths of signals, and one road signal is exported as the output signal, and another road signal feedback is given attenuator;
Attenuator is used to receive the signal that directional coupler feeds back, and this feedback signal is decayed;
Low-converter is used for the deamplification of attenuator output is down-converted to baseband signal;
Analog to digital converter is used for converting the baseband signal of low-converter output into digital signal, as feedback loop output signal;
Delayer is used for original input signal is delayed time, and generates delay input signal; And
The predistorting data renovator; Be used to receive the delay input signal that feedback loop output signal that analog to digital converter exports and delayer are exported, and question blank function and filter coefficient carried out online updating according to feedback loop output signal that is received and delay input signal.
27. power amplification device according to claim 26, wherein the predistorting data renovator is configured to question blank function and the filter function according to the arbitrary described identifying inverse characteristic of nonlinear system method online updating predistortion module of claim 1～8.
28. power amplification device according to claim 26, wherein the predistorting data renovator is configured to the question blank function and the filter function of online updating predistortion module in the following manner:
Relatively original input signal x (n) and feedback loop output signal, according to following formula calculation error signal:
Wherein, F{}, Q{}, P{} represent filter function, question blank function and power amplifier function respectively, and
z(n)＝x(n)·Q([|x(n)|])，
Question blank function Q { } does
Q([|x(n)|])＝[q
0，q
1，...，q
K-1]
T
Filter coefficient does
W＝[w
1，w
2，...，w
L-1]
T
L represents the memory depth of filter, and [||] represented delivery and quantification; And
According to following formula online updating question blank function and filter coefficient
Wherein
U＝[μ
1，μ
1，...，μ
L-1]
T
U and μ
qBe the convergence step-length.
29. the method for the parameter of the predistortion module of an online updating power amplification device comprises step:
Relatively the original input signal x (n) and the feedback loop output signal of power amplification device, according to following formula calculation error signal:
Wherein, F{}, Q{}, P{} represent the filter function of filter, the question blank function of question blank and the magnification function of power amplifier respectively, and
z(n)＝x(n)·Q([|x(n)|])，
Question blank function Q { } does
Q([|x(n)|])＝[q
0，q
1，...，q
K-1]
T
Filter coefficient does
W＝[w
1，w
2，...，w
L-1]
T
L represents the memory depth of filter, and [||] represented delivery and quantification; And
According to following formula online updating question blank function and filter coefficient
Wherein
U＝[μ
1，μ
1，...，μ
L-1]
T
U and μ
qBe the convergence step-length.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200810081674A CN101527544B (en) | 2008-03-05 | 2008-03-05 | Device and method for identifying inverse characteristic of nonlinear system, power amplifier and predistorter thereof |
| JP2008248388A JP5120178B2 (en) | 2008-03-05 | 2008-09-26 | Nonlinear system inverse characteristic identification device and method, power amplification device, and power amplifier predistorter |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200810081674A CN101527544B (en) | 2008-03-05 | 2008-03-05 | Device and method for identifying inverse characteristic of nonlinear system, power amplifier and predistorter thereof |
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| CN101527544B true CN101527544B (en) | 2012-09-12 |
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Families Citing this family (21)
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| JP5221260B2 (en) * | 2008-09-22 | 2013-06-26 | 日本無線株式会社 | Predistorter |
| CN102025327B (en) * | 2009-09-18 | 2013-01-02 | 富士通株式会社 | Amplifier apparatus and predistortion control method |
| JP5516378B2 (en) | 2010-12-13 | 2014-06-11 | 富士通株式会社 | Distortion compensation apparatus, distortion compensation method, and radio apparatus |
| JP5707999B2 (en) * | 2011-02-09 | 2015-04-30 | 富士通株式会社 | Distortion compensation apparatus, transmitter, and distortion compensation method |
| JP5751056B2 (en) * | 2011-07-14 | 2015-07-22 | 富士通株式会社 | Distortion compensation apparatus, transmitter, and distortion compensation method |
| EP2582043A1 (en) * | 2011-10-10 | 2013-04-17 | Astrium Limited | Control system for a power amplifier |
| CN103580617A (en) * | 2012-07-20 | 2014-02-12 | 富士通株式会社 | Method and device for training predistorter |
| CN103780206A (en) * | 2012-10-24 | 2014-05-07 | 华为技术有限公司 | Feedback link and implementation method thereof |
| JP6007744B2 (en) * | 2012-11-20 | 2016-10-12 | 富士通株式会社 | Distortion compensation apparatus, transmission apparatus, distortion compensation method, and transfer function calculation method |
| JP6064714B2 (en) * | 2013-03-19 | 2017-01-25 | 富士通株式会社 | Distortion compensation apparatus and distortion compensation method |
| JP6221518B2 (en) | 2013-05-24 | 2017-11-01 | 富士通株式会社 | Power amplification apparatus, transmitter, and power amplification apparatus control method |
| CN103888312B (en) * | 2014-03-04 | 2017-06-09 | 京信通信系统（广州）有限公司 | The alarm method and device of a kind of pre-distortion system |
| CN104242834B (en) * | 2014-08-15 | 2017-03-08 | 中国舰船研究设计中心 | Receiver preamplifier nonlinear response modeling method based on higher order polynomial-fitting |
| CN104486282B (en) * | 2014-12-15 | 2018-02-13 | 成都康特数字广播电视设备有限公司 | Radio frequency multicarrier intermodulation restraining device |
| US9484962B1 (en) * | 2015-06-05 | 2016-11-01 | Infineon Technologies Ag | Device and method for adaptive digital pre-distortion |
| CN107786174B (en) * | 2016-08-26 | 2021-02-05 | 中颖电子股份有限公司 | Circuit and method for predistortion |
| CN108011598B (en) * | 2017-12-26 | 2021-04-27 | 中国工程物理研究院电子工程研究所 | Digital predistortion adaptive processing method |
| US10659124B2 (en) | 2018-03-01 | 2020-05-19 | Fujitsu Limited | Multiantenna communication device and coefficient update method |
| JP2019201347A (en) | 2018-05-17 | 2019-11-21 | 富士通株式会社 | Distortion compensation device and distortion compensation method |
| JP2019201361A (en) | 2018-05-17 | 2019-11-21 | 富士通株式会社 | Distortion compensation apparatus and distortion compensation method |
| CN114421902B (en) * | 2022-01-21 | 2023-06-06 | 上海物骐微电子有限公司 | Predistortion calibration method and application suitable for WiFi memory-free power amplifier |
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| CN1523751A (en) * | 2003-02-21 | 2004-08-25 | 日本电气株式会社 | Circuit and method for compensating for nonliner distortion of power amplifier |
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| Publication number | Publication date |
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| CN101527544A (en) | 2009-09-09 |
| JP5120178B2 (en) | 2013-01-16 |
| JP2009213113A (en) | 2009-09-17 |
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