Digital multi-beam-oriented pre-distortion processing method, device, equipment and medium

Abstract

The invention relates to a digital multi-beam-oriented pre-distortion processing method and device, equipment and a medium. The method comprises the following steps: carrying out pre-distortion and beamforming processing on multiple paths of input signals, amplifying the beamformed signals through a power amplifier of a corresponding channel, and then sending out the beamformed signals; performing first estimation processing on the received signals to obtain multiple paths of received signals; establishing a pre-distortion model according to multi-beam signal distortion characteristics, and performing second estimation processing on multiple paths of received signals to obtain a static nonlinear function model matrix; performing third estimation processing through a pre-distortion model to obtain multiple paths of estimated input signals; and learning and updating a pre-distortion model according to an error signal between the multi-channel pre-distorted signal and the multi-channel estimated input signal. Therefore, the complexity of the model is reduced, the balance of the complexity and the performance is realized, the model parameters of the pre-distortion model are continuously updated in combination with the spatial domain scene and the multi-beam signal distortion characteristics, and the signal pre-distortion processing effect is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a pre-distortion processing method, apparatus, computer equipment, computer-readable storage medium and computer program product for digital multi-beam. Background Art

[0002] High-throughput satellites mainly rely on multi-beam technology to achieve spatial freedom multiplexing of the array to save spectrum resources.

[0003] In traditional technology, when multi-beam pre-distortion processing is performed based on digital technology, in order to avoid inter-band intermodulation distortion, the multi-beam requires the power amplifier to operate in the linear region, and the working efficiency of the power amplifier is low. For satellite-borne phased arrays with harsh environments, power amplifier efficiency is equally important. In order to solve the intermodulation distortion problem and achieve higher power amplifier efficiency while ensuring signal linearization, linearization technology is required. For a single power amplifier, the digital pre-distortion method (Digital Pre-Distortion, DPD) is an effective linearization solution to the distortion problem, but in the multi-beam phased array scenario, it is necessary to consider the nonlinear characteristics and re-model them, and study the corresponding pre-distortion solutions.

[0004] However, the traditional multi-beam structure is essentially a continuation of the traditional pre-distortion method at the beam level. When calculating the static nonlinear function, the model terms are repeatedly calculated, resulting in structural redundancy. The combination of spatial scenarios and signal characteristics is not considered, resulting in an unbalanced performance and complexity of the pre-distortion model. Summary of the invention

[0005] Based on this, it is necessary to provide a digital multi-beam pre-distortion processing method, apparatus, computer equipment, computer-readable storage medium and computer program product that can effectively balance the performance and complexity of the pre-distortion model in order to address the above technical problems.

[0006] In a first aspect, the present application provides a predistortion processing method for digital multi-beam, comprising:

[0007] Performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0008] Performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals;

[0009] Each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array;

[0010] The signal sent by the transmitting antenna array is received through the receiving antenna or the feedback unit, and the received signal is first estimated and processed to obtain a multi-path receiving signal;

[0011] A pre-distortion model is established according to the distortion characteristics of the multi-beam signal, and a second estimation process is performed on the multi-path received signal according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix;

[0012] Performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal;

[0013] According to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal, the predistortion parameters in the predistortion model are learned and updated.

[0014] In one embodiment, the performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals includes:

[0015] Through P uniformly distributed linear array elements, beamforming processing is performed on L pre-distortion processed signals to obtain P beamformed signals, where each beamformed signal is related to the weight of the corresponding array element, and P and L are natural numbers greater than 1.

[0016] In one embodiment, the receiving of the signal sent by the transmitting antenna array by the receiving antenna or the feedback unit, and performing a first estimation process on the received signal to obtain a multi-path receiving signal includes:

[0017] Performing digital preprocessing on the signal received by the receiving antenna or the feedback unit, the digital preprocessing including: performing at least one of frequency conversion and digital sampling processing on the received signal;

[0018] The processed digital signals are synthesized or synthesized through receiving antennas to obtain multi-path receiving signals pointing to preset angles.

[0019] In one embodiment, establishing a pre-distortion model according to the distortion characteristics of the multi-beam signal includes:

[0020] A memory polynomial model is established based on the merging of multi-path received signals, and a pre-distortion model is established in combination with the third-order intermodulation terms generated between beams;

[0021] According to the intra-band intermodulation components of the multi-beams, the predistortion order, and the number of inter-band third-order intermodulation distortion items, the multi-path input signal items in the predistortion model are excluded, so that each signal in the predistortion model has a common intermodulation distortion amount.

[0022] In one embodiment, the multi-path received signal is estimated for the second time according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix, including:

[0023] Performing point multiplication processing on the intermediate quantity of the predistortion model and the multiple input signals to obtain an updated intermediate quantity;

[0024] The updated intermediate quantities are concatenated through dimensions to obtain a static nonlinear function model matrix.

[0025] In one embodiment, the learning and updating of the predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal includes:

[0026] The predistortion parameters in the predistortion model are combined and replaced by a predistortion model parameter matrix;

[0027] Representing the multi-channel receiving signals in matrix form to obtain a receiving signal matrix;

[0028] Performing a point multiplication process on the received signal matrix and the static nonlinear function model matrix to obtain a static nonlinear function matrix;

[0029] Based on the predistortion model parameter matrix, the static nonlinear function matrix, and the multi-channel input signals, a calculation formula for the error signal between the multi-channel predistortion processed signals and the multi-channel estimated input signals is constructed;

[0030] A cost function is constructed according to the mean square error criterion to obtain the iteration step length corresponding to the predistortion model parameter matrix;

[0031] According to the iteration step length, the predistortion model parameter matrix is ​​learned and updated.

[0032] In a second aspect, the present application further provides a pre-distortion processing device for digital multi-beam, the device comprising:

[0033] A predistortion processing module, used to perform predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0034] A beamforming module, used for performing beamforming processing on the multi-path pre-distortion processed signals to obtain a plurality of beamformed signals;

[0035] An amplification module, used to amplify each beamformed signal through a power amplifier of a corresponding channel and send it out through a transmitting antenna array;

[0036] A first estimation module is used to receive a signal sent by a transmitting antenna array through a receiving antenna or a feedback unit, and perform a first estimation process on the received signal to obtain a multi-path receiving signal;

[0037] A second estimation module is used to establish a pre-distortion model according to the distortion characteristics of the multi-beam signal, and perform a second estimation process on the multi-path received signal according to the distortion characteristics of the multi-beam signal through the pre-distortion model to obtain a static nonlinear function model matrix;

[0038] A third estimation module, configured to perform a third estimation process based on the static nonlinear function model matrix and the multi-path received signal through the pre-distortion model to obtain a multi-path estimated input signal;

[0039] The updating module is used for learning and updating the predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal.

[0040] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] Performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0042] Performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals;

[0043] Each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array;

[0044] The signal sent by the transmitting antenna array is received through the receiving antenna or the feedback unit, and the received signal is first estimated and processed to obtain a multi-path receiving signal;

[0045] A pre-distortion model is established according to the distortion characteristics of the multi-beam signal, and a second estimation process is performed on the multi-path received signal according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix;

[0046] Performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal;

[0047] According to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal, the predistortion parameters in the predistortion model are learned and updated.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0049] Performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0050] Performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals;

[0051] Each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array;

[0052] The signal sent by the transmitting antenna array is received through the receiving antenna or the feedback unit, and the received signal is first estimated and processed to obtain a multi-path receiving signal;

[0053] A pre-distortion model is established according to the distortion characteristics of the multi-beam signal, and a second estimation process is performed on the multi-path received signal according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix;

[0054] Performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal;

[0055] According to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal, the predistortion parameters in the predistortion model are learned and updated.

[0056] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0057] Performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0058] Performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals;

[0059] Each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array;

[0060] The signal sent by the transmitting antenna array is received through the receiving antenna or the feedback unit, and the received signal is first estimated and processed to obtain a multi-path receiving signal;

[0061] A pre-distortion model is established according to the distortion characteristics of the multi-beam signal, and a second estimation process is performed on the multi-path received signal according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix;

[0062] Performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal;

[0063] According to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal, the predistortion parameters in the predistortion model are learned and updated.

[0064] The above-mentioned predistortion processing method, device, computer equipment, computer-readable storage medium and computer program product for digital multi-beam obtain corresponding multi-channel predistortion processed signals by predistortion processing on multi-channel input signals; thus, the input signals can be predistorted in advance to improve the distortion of subsequent beamforming signals. Beamforming processing is performed on the multi-channel predistortion processed signals to obtain multiple beamformed signals; each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array; thus, the sent signal can be enhanced to improve the signal quality received by the receiving end. The signal sent by the transmitting antenna array is received by the receiving antenna or the feedback unit, and the received signal is first estimated to obtain a multi-channel received signal; thus, the signal interference of adjacent channels can be suppressed. A predistortion model is established according to the distortion characteristics of the multi-beam signal, and the multi-channel received signal is second estimated according to the distortion characteristics of the multi-beam signal through the predistortion model to obtain a static nonlinear function model matrix; thus, the complexity of the multi-channel predistortion model is reduced, which is conducive to the realization of multi-beam on board. The pre-distortion model performs a third estimation process based on the static nonlinear function model matrix and the multi-channel received signal to obtain a multi-channel estimated input signal; thereby unifying the multi-beam model terms so that each signal has a common intermodulation distortion. According to the error signal between the multi-channel pre-distortion processed signal and the multi-channel estimated input signal, the pre-distortion parameters in the pre-distortion model are learned and updated. This greatly reduces the complexity of the pre-distortion model, achieves a balance between complexity and model performance, and combines the spatial scenario with the distortion characteristics of the multi-beam signal to continuously update the model parameters of the pre-distortion model, thereby improving the effect of signal pre-distortion processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 is a schematic diagram of the principle of a predistortion processing method for digital multi-beam in one embodiment;

[0067] Figure 2 1 is a flow chart of a predistortion processing method for digital multi-beam in one embodiment;

[0068] Figure 3 Schematic diagram of the principle of an iterative method for a static nonlinear function model matrix in one embodiment;

[0069] Figure 4 A schematic diagram of received power spectrum density of a multi-beam scenario in which pre-distortion processing is performed using different methods in one embodiment of the present application;

[0070] Figure 5 is a structural block diagram of a predistortion processing device for digital multi-beam in one embodiment;

[0071] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0073] In order to facilitate understanding of the technical solutions in various embodiments of the present application, the following brief description of the professional terms involved in the present application is first made:

[0074] 1) A power amplifier (PA) is an amplifier that can generate maximum power output to drive a load (such as a speaker) under given distortion conditions. Its working principle is to use the current control of a transistor or the voltage control of a field effect transistor to convert the power of a power supply into a current that changes according to the input signal. The power amplifier itself has nonlinear characteristics. At a higher peak-to-average ratio, the signal will fall into the nonlinear amplification area, and the signal will be distorted. The distortion of the signal is reflected in the channel as in-band distortion, that is, the offset of amplitude and phase, which eventually leads to the offset of the constellation diagram. The nonlinearity of the power amplifier is reflected outside the channel as band expansion, generating new frequency components such as harmonics and intermodulation, and causing part of the power to leak into adjacent channels. Both in-band distortion and out-of-band distortion will affect normal communication and lead to an increase in bit error rate. Under large-scale antenna arrays, out-of-band distortion is most serious in the main beam direction, because nonlinear distortion is also affected by beamforming, the intensity in the main beam direction is the highest, and the intensity in other directions is lower.

[0075] 2) Digital Pre-Distortion (DPD) refers to the digital processing of the signal, which is the inverse processing of the nonlinear characteristics of the signal before the signal is input into the power amplifier. Finally, the pre-distorted signal and the nonlinear characteristics of the power amplifier are superimposed to obtain a linearized output signal.

[0076] 3) Memory effect refers to the fact that the output of the system is related to the input of historical time. As the peak-to-average ratio of the signal increases, the impact of the memory effect becomes more significant. The memory polynomial is a nonlinear behavior model of power amplifier memory and a simplification of the Volterra model. It is widely used in pre-distortion technology.

[0077] For example, Figure 1 FIG. 1 is a schematic diagram of a principle of a predistortion processing method for digital multi-beam in an embodiment. Figure 1 As shown, at the sending end, x1(n), x2(n), ..., x L (n) represent L input signals respectively. Each input signal is predistorted to obtain L predistorted signals z1(n), z2(n), …, z l (n); then, beamforming is performed on the multi-path predistortion processed signals to obtain P beamformed signals s1(n), s2(n), ..., s P (n); each beamformed signal passes through the power amplifier of the corresponding channel (such as Figure 1 The figure shows that the P power amplifiers (PA1, PA2, ..., PAP) are used for amplification and P signals (y1(n), y2(n), ..., y1(n)) are sent out through the transmitting antenna array. P(n). At the receiving end, the receiving antenna or feedback unit receives the signal sent by the transmitting antenna array and performs a first estimation process on the received signal to obtain L received signals. A predistortion model is established according to the distortion characteristics of the multi-beam signal, and the multi-path received signal is subjected to a second estimation process according to the distortion characteristics of the multi-beam signal by the predistortion model to obtain static nonlinear function model matrices u(n) and u′(n); and a static nonlinear function model matrix u(n) and u′(n) are estimated based on the predistortion model and the L-path received signal. Perform the third estimation process to obtain the input signal of L-way estimation According to the L-path predistortion processed signals z1(n), z2(n), ..., z L (n) and the input signal of the multipath estimation The error signals e1(n), e2(n), ..., e L (n), learning and updating the predistortion parameters in the predistortion model.

[0078] In an exemplary embodiment, Figure 2 As shown, a predistortion processing method for digital multi-beam is provided. The method in this embodiment may include the following steps 201 to 207. Among them:

[0079] Step 201 , performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals.

[0080] In this embodiment, it can be combined Figure 1 As shown in the figure, the transmit signal is modulated in the digital domain by a digital beamformer, then amplified by the power amplifier of each channel, and then sent out through the transmit antenna. Assume that the transmitter uses P uniformly distributed linear arrays with a spacing of half a wavelength d. The signal after beamforming is set to s p (n)(p=1,2,…,P), the weight of the pth array element is w p , then the signal after beamforming on each channel is:

[0081] s p (n) = w p x(n).

[0082] Step 202 , performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals.

[0083] Exemplary, combined Figure 1As shown, beamforming processing is performed on L pre-distortion processed signals through P uniformly distributed linear array elements to obtain P beamformed signals, wherein each beamformed signal is related to the weight of the corresponding array element, and P and L are natural numbers greater than 1.

[0084] Step 203: amplify each beamformed signal through a power amplifier of a corresponding channel and send it out through a transmitting antenna array.

[0085] In this embodiment, the memory polynomial model is used as an example to analyze the power amplifier. The output y of the Pth channel after being amplified by the PA is p (n) is:

[0086]

[0087] Among them, m and k are the memory depth and the polynomial order respectively, M and K are the total value of the memory depth and the total order of the polynomial respectively, is the nonlinear model parameter of the Pth channel. p (n) = w p x(n), we can get:

[0088]

[0089] The phase of the array weight is (p-1)β, that is, arg(w p |)=(p-1)β. When the array element p, order k and memory depth m are determined, is a constant.

[0090] Exemplarily, digital preprocessing is performed on the signal received by the receiving antenna or the feedback unit, and the digital preprocessing includes but is not limited to: frequency conversion, digital sampling, etc. of the received signal; synthesis processing is performed on the processed digital signal or synthesis processing is performed through the receiving antenna to obtain a multi-path receiving signal pointing to a preset angle.

[0091] Step 204: Receive the signal sent by the transmitting antenna array through the receiving antenna or the feedback unit, and perform a first estimation process on the received signal to obtain a multi-path receiving signal.

[0092] In this embodiment, at the receiving end, the receiving antenna synthesizes the signals of P channels. When the beam points to θ, the calculation formula of the received signal can be expressed as:

[0093]

[0094] in, By exchanging and ordering the calculation formulas for the above received signals, we can obtain:

[0095]

[0096] in,

[0097] Step 205: establish a predistortion model according to the distortion characteristics of the multi-beam signal, and perform a second estimation process on the multi-path received signals according to the distortion characteristics of the multi-beam signal through the predistortion model to obtain a static nonlinear function model matrix.

[0098] Exemplarily, a memory polynomial model is established based on the merging of multiple received signals, and a pre-distortion model is established in combination with the third-order intermodulation terms generated between each beam; according to the in-band intermodulation components of the multiple beams, the pre-distortion order, and the number of third-order intermodulation distortion terms between bands, the multiple input signal terms in the pre-distortion model are excluded, so that each signal in the pre-distortion model has a common intermodulation distortion amount.

[0099] In this embodiment, the received signal can be regarded as a new memory polynomial model, c mk is the new model parameter. The model is established by considering the third-order intermodulation terms between the beams as follows:

[0100]

[0101] Among them, u k (nm) represents the in-band intermodulation component between multiple beams, and K≥k1≥k2≥…≥k L , K is the conventional predistortion order, K' is the number of third-order intermodulation distortion items, β l is the coefficient of the third-order intermodulation term between bands. k (n) exclude x l (n) term, so that u in the calculation formula of the pre-distortion model k (nm) is independent of l, that is, the model items of each beam are the same, and the parameter learning stage only extracts the model parameters of each beam.

[0102] In this embodiment, the signals of the predistortion model have a common intermodulation distortion, and the multi-beam model items are unified to obtain the estimated received signal according to the beamforming principle. The input signal is further estimated by the predistortion model The parameter model is learned from the error signal e(n).

[0103] For example, Figure 3 As shown, in Figure 3 The iterative method of the static nonlinear function model matrix is ​​shown in the figure. Taking u(n) as an example, the intermediate quantities [t1(n), t2(n), ..., t L (n)], [t2(n), t3(n),…,t L(n)], …, [t L (n)] and multiple input signals x1(n), x2(n), …, x L The absolute value of (n) (symbol |·| in the figure) is multiplied (symbol x in the figure) to obtain the updated intermediate quantities t1(n), t2(n), ..., t L (n); the updated intermediate quantities are connected in series through the dimension to obtain the static nonlinear function model matrix u(n). The calculation method of u′(n) is similar, except that the input signals x1(n), x2(n), …, x L (n) is processed by taking the absolute value, so it will not be described in detail.

[0104] Step 206 , performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal.

[0105] The predistortion model in this embodiment is equivalent to the post-inverse model of the power amplifier. Exemplarily, the process of updating the predistortion model coefficients can be obtained by collecting the forward and feedback data of the power amplifier. By accurately simulating the behavior of the post-inverse model of the power amplifier, the ability of the predistortion model to linearize the power amplifier is improved.

[0106] Step 207: learning and updating predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal.

[0107] Exemplarily, the predistortion parameter C in the predistortion model is mlk and β l Merge and replace with the predistortion model parameter matrix α; express the multi-channel received signals in matrix form to obtain the received signal matrix The received signal matrix is ​​multiplied by the static nonlinear function model matrix to obtain the static nonlinear function matrix; based on the predistortion model parameter matrix α, the static nonlinear function matrix, and the multiple input signals z2(n), ..., z L (n), construct error signals e1(n), e2(n), ..., e between the multi-path predistortion processed signal and the multi-path estimated input signal L (n); construct a cost function according to the mean square error criterion to obtain the iteration step corresponding to the pre-distortion model parameter matrix; according to the iteration step, learn to update the pre-distortion model parameter matrix.

[0108] In this embodiment, the predistortion parameter c lmk , β l Merge and replace with the matrix α of the predistortion model parameters, Where K αis the total number of items after K and K' are combined, and a similar pre-distortion matrix is ​​considered as follows:

[0109] z l (n) = x u (n)·α,

[0110] Where: x u (n) is the parallel form of multiple input signals:

[0111]

[0112] is a static nonlinear function matrix on a certain frequency band, is the third-order intermodulation distortion term generated by multi-path signals. Assume that the static nonlinear function model matrix is: u(n) = [u1(n),u2(n),…,u K (n)] T ,u'(n)=[u'1(n),u'2(n),…,u' K' (n)] T , then x ul (n), x u'l (n) can be expressed as:

[0113]

[0114] Further, consider the predistorted output sequence z l The sequence of static nonlinear functions corresponding to the model Considering a similar predistortion matrix form, we can get:

[0115] y l =X l ·α.

[0116] Among them, the multi-input static nonlinear function u(n) can be obtained according to Figure 3 The calculation method of u'(n) is similar to the pre-distortion model block diagram shown in the figure. In the figure, × represents the dot product of two signals, t l (n) is the intermediate quantity of model update, and its initial value is x l (n), iteratively obtained t l (n) is concatenated into u(n) through the dimensions.

[0117] In this embodiment, the method of learning the parameter model of the error signal is not unique, and the predistortion parameter update formula in the predistortion model can be derived using a typical least mean square algorithm. Considering that the error signal expression is:

[0118]

[0119] in is the received signal matrix form, and α is the parameter matrix composed of predistortion parameters. In order to find a set of α that makes the output signal closer to the input signal, a cost function is constructed according to the mean square error criterion, and the step size iteration formula is obtained:

[0120] α(n+1)=α(n)+μY R H e H .

[0121] Among them, α(n) is the parameter matrix composed of the nth predistortion parameters, α(n+1) is the parameter matrix composed of the n+1th predistortion parameters, μ represents the coefficient of the cost function, is the conjugate transposed matrix of the received signal, is the conjugate transposed matrix of the error signal.

[0122] For example, Figure 4 FIG. 1 is a schematic diagram of the received power spectrum density of a multi-beam scene in which different methods for pre-distortion processing are performed in an embodiment of the present application. Figure 4 As shown, compared with several other pre-distortion algorithms, the pre-distortion processing method for digital multi-beam provided in this embodiment is close to the ideal multi-channel pre-distortion algorithm, and considering the spatial processing scenario, it has certain advantages in the ACPR suppression capability on the side affected by adjacent channel interference. Thus, the complexity is greatly reduced while the digital pre-distortion model provides similar performance, achieving a balance between complexity and performance.

[0123] This embodiment is carried out according to the digital pre-distortion technology, improves the traditional memory polynomial, designs a nonlinear distortion model according to the characteristics of simultaneous multi-beam, and derives a parallel pre-distortion model update method. Compared with the traditional pre-distortion model, it has more advantages in balancing performance and complexity.

[0124] In the above-mentioned predistortion processing method for digital multi-beam, by predistortion processing of multi-channel input signals, corresponding multi-channel predistortion processed signals are obtained; thus, the input signals can be predistorted in advance to improve the distortion of subsequent beamforming signals. Beamforming processing is performed on the multi-channel predistortion processed signals to obtain multiple beamformed signals; each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array; thus, the sent signal can be enhanced to improve the signal quality received by the receiving end. The signal sent by the transmitting antenna array is received by the receiving antenna or the feedback unit, and the received signal is first estimated to obtain a multi-channel received signal; thus, the signal interference of adjacent channels can be suppressed. A predistortion model is established according to the distortion characteristics of the multi-beam signal, and the multi-channel received signal is second estimated according to the distortion characteristics of the multi-beam signal through the predistortion model to obtain a static nonlinear function model matrix; thus, the complexity of the multi-channel predistortion model is reduced, which is conducive to the realization of multi-beam onboard. The pre-distortion model is used to perform a third estimation process based on the static nonlinear function model matrix and the multi-channel received signal to obtain a multi-channel estimated input signal; thereby unifying the multi-beam model terms so that each signal has a common intermodulation distortion. According to the error signal between the multi-channel pre-distortion processed signal and the multi-channel estimated input signal, the pre-distortion parameters in the pre-distortion model are learned and updated. This greatly reduces the complexity of the pre-distortion model, achieves a balance between complexity and model performance, and combines the spatial scenario with the multi-beam signal distortion characteristics to continuously update the model parameters of the pre-distortion model, thereby improving the effect of signal pre-distortion processing.

[0125] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a digital multi-beam oriented predistortion processing device for implementing the above-mentioned digital multi-beam oriented predistortion processing method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more digital multi-beam oriented predistortion processing devices provided below can refer to the limitations of the digital multi-beam oriented predistortion processing method in the above text, and will not be repeated here.

[0127] In an exemplary embodiment, Figure 5 As shown, a predistortion processing device for digital multi-beam is provided, comprising: a predistortion processing module 501, a beamforming module 502, an amplification module 503, a first estimation module 504, a second estimation module 505, a third estimation module 506 and an updating module 507, wherein:

[0128] The predistortion processing module 501 is used to perform predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals;

[0129] A beamforming module 502, configured to perform beamforming processing on the multi-path pre-distortion processed signals to obtain a plurality of beamformed signals;

[0130] an amplification module 503, configured to amplify each beamformed signal through a power amplifier of a corresponding channel and transmit the signal through a transmitting antenna array;

[0131] A first estimation module 504 is used to receive a signal sent by a transmitting antenna array through a receiving antenna or a feedback unit, and perform a first estimation process on the received signal to obtain a multi-path receiving signal;

[0132] The second estimation module 505 is used to establish a pre-distortion model according to the distortion characteristics of the multi-beam signal, and perform a second estimation process on the multi-path received signal according to the distortion characteristics of the multi-beam signal through the pre-distortion model to obtain a static nonlinear function model matrix;

[0133] A third estimation module 506 is used to perform a third estimation process based on the static nonlinear function model matrix and the multi-path received signal through a pre-distortion model to obtain a multi-path estimated input signal;

[0134] The updating module 507 is used to learn and update the predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal.

[0135] Exemplarily, the beamforming module 502 is specifically used to: perform beamforming processing on L pre-distortion processed signals through P uniformly distributed linear array elements to obtain P beamformed signals, wherein each beamformed signal is related to the weight of the corresponding array element, and P and L are natural numbers greater than 1.

[0136] Exemplarily, the first estimation module 504 is specifically used to: perform digital preprocessing on the signal received by the receiving antenna or the feedback unit, the digital preprocessing including: frequency conversion of the received signal and at least one of digital sampling; synthesizing the processed digital signal or performing synthesis processing through the receiving antenna to obtain a multi-path received signal pointing to a preset angle.

[0137] Exemplarily, the pre-distortion model is established according to the distortion characteristics of the multi-beam signal, including: establishing a memory polynomial model based on the merging of multi-path received signals, and establishing a pre-distortion model in combination with the third-order intermodulation terms generated between the beams; according to the in-band intermodulation components of the multi-beams, the pre-distortion order, and the number of third-order intermodulation distortion terms between the bands, the multi-path input signal terms in the pre-distortion model are excluded, so that each signal in the pre-distortion model has a common intermodulation distortion amount.

[0138] Exemplarily, the second estimation module 505 is specifically used to: perform point multiplication processing on the intermediate quantity of the pre-distortion model and the multi-channel input signal to obtain an updated intermediate quantity; and connect the updated intermediate quantity in series through dimensions to obtain a static nonlinear function model matrix.

[0139] Exemplarily, the updating module 507 is specifically used to: merge the predistortion parameters in the predistortion model and replace them with the predistortion model parameter matrix; represent the multi-channel received signals in matrix form to obtain a received signal matrix; perform point multiplication processing on the received signal matrix and the static nonlinear function model matrix to obtain a static nonlinear function matrix; construct a calculation formula for the error signal between the multi-channel predistortion processed signals and the multi-channel estimated input signals based on the predistortion model parameter matrix, the static nonlinear function matrix, and the multi-channel input signals; construct a cost function according to the mean square error criterion to obtain the iteration step corresponding to the predistortion model parameter matrix; and learn to update the predistortion model parameter matrix according to the iteration step.

[0140] Each module in the above-mentioned digital multi-beam predistortion processing device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0141] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a pre-distortion processing method for digital multi-beam is implemented.

[0142] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0145] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0147] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A predistortion processing method for digital multi-beam, characterized in that: The method comprises: Performing predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals; Performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals; Each beamformed signal is amplified by the power amplifier of the corresponding channel and sent out through the transmitting antenna array; The signal sent by the transmitting antenna array is received through the receiving antenna or the feedback unit, and the received signal is first estimated and processed to obtain a multi-path receiving signal; A pre-distortion model is established according to the distortion characteristics of the multi-beam signal, and a second estimation process is performed on the multi-path received signal according to the distortion characteristics of the multi-beam signal by using the pre-distortion model to obtain a static nonlinear function model matrix; Performing a third estimation process based on the static nonlinear function model matrix and the multi-path received signals through the pre-distortion model to obtain a multi-path estimated input signal; According to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal, the predistortion parameters in the predistortion model are learned and updated.

2. The method according to claim 1, characterized in that The performing beamforming processing on the multi-path predistortion processed signals to obtain a plurality of beamformed signals includes: Through P uniformly distributed linear array elements, beamforming processing is performed on L pre-distortion processed signals to obtain P beamformed signals, where each beamformed signal is related to the weight of the corresponding array element, and P and L are natural numbers greater than 1.

3. The method according to claim 1, characterized in that The receiving of the signal sent by the transmitting antenna array by the receiving antenna or the feedback unit, and performing a first estimation process on the received signal to obtain a multi-path receiving signal, includes: Performing digital preprocessing on the signal received by the receiving antenna or the feedback unit, wherein the digital preprocessing includes: performing at least one of frequency conversion and digital sampling processing on the received signal; The processed digital signals are synthesized or synthesized through receiving antennas to obtain multi-path receiving signals pointing to preset angles.

4. The method according to claim 1, characterized in that: The pre-distortion model is established according to the distortion characteristics of the multi-beam signal, comprising: A memory polynomial model is established based on the merging of multi-path received signals, and a pre-distortion model is established in combination with the third-order intermodulation terms generated between beams; According to the intra-band intermodulation components of the multi-beams, the predistortion order, and the number of inter-band third-order intermodulation distortion items, the multi-path input signal items in the predistortion model are excluded, so that each signal in the predistortion model has a common intermodulation distortion amount.

5. The method according to any one of claims 1 to 4, characterized in that: The multi-path received signal is subjected to a second estimation process according to the distortion characteristics of the multi-beam signal by the pre-distortion model to obtain a static nonlinear function model matrix, including: Performing point multiplication processing on the intermediate quantity of the predistortion model and the multiple input signals to obtain an updated intermediate quantity; The updated intermediate quantities are concatenated through dimensions to obtain a static nonlinear function model matrix.

6. The method according to any one of claims 1 to 4, characterized in that: The learning and updating of the predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal comprises: The predistortion parameters in the predistortion model are combined and replaced by a predistortion model parameter matrix; Representing the multi-channel receiving signals in matrix form to obtain a receiving signal matrix; Performing a point multiplication process on the received signal matrix and the static nonlinear function model matrix to obtain a static nonlinear function matrix; Based on the predistortion model parameter matrix, the static nonlinear function matrix, and the multi-channel input signals, a calculation formula for the error signal between the multi-channel predistortion processed signals and the multi-channel estimated input signals is constructed; A cost function is constructed according to the mean square error criterion to obtain the iteration step length corresponding to the predistortion model parameter matrix; According to the iteration step length, the predistortion model parameter matrix is ​​learned and updated.

7. A predistortion processing device for digital multi-beam, characterized in that: The device comprises: A predistortion processing module, used to perform predistortion processing on multiple input signals to obtain corresponding multiple predistortion processed signals; A beamforming module, used for performing beamforming processing on the multi-path pre-distortion processed signals to obtain a plurality of beamformed signals; An amplification module, used to amplify each beamformed signal through a power amplifier of a corresponding channel and send it out through a transmitting antenna array; A first estimation module is used to receive a signal sent by a transmitting antenna array through a receiving antenna or a feedback unit, and perform a first estimation process on the received signal to obtain a multi-path receiving signal; A second estimation module is used to establish a pre-distortion model according to the distortion characteristics of the multi-beam signal, and perform a second estimation process on the multi-path received signal according to the distortion characteristics of the multi-beam signal through the pre-distortion model to obtain a static nonlinear function model matrix; A third estimation module, configured to perform a third estimation process based on the static nonlinear function model matrix and the multi-path received signal through the pre-distortion model to obtain a multi-path estimated input signal; The updating module is used for learning and updating the predistortion parameters in the predistortion model according to the error signal between the multi-path predistortion processed signal and the multi-path estimated input signal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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