CN116389203A

CN116389203A - Nonlinear correction method, device and system

Info

Publication number: CN116389203A
Application number: CN202111592222.3A
Authority: CN
Inventors: 李弘旻; 刘发林; 刘乔
Original assignee: University of Science and Technology of China USTC; Huawei Technologies Co Ltd
Current assignee: University of Science and Technology of China USTC; Huawei Technologies Co Ltd
Priority date: 2021-12-23
Filing date: 2021-12-23
Publication date: 2023-07-04
Also published as: WO2023116834A1

Abstract

The application discloses a nonlinear correction method, device and system, relates to the technical field of signal processing, and can improve nonlinear correction performance of a predistortion device. According to the scheme, the coefficient-perception-based digital predistortion device is introduced into the transmitter, the regression matrix is generated, orthogonalization processing is carried out on the regression matrix, the orthogonalization basis function is generated, the output signal is obtained, the output signal is associated with the parameters of the power amplification model according to the orthogonalization basis function generation, the output signal is sampled so as to realize extraction of the parameters of the power amplification model, prediction of power amplification output under the condition of full sampling is realized, and then nonlinear correction parameters, such as predistortion parameters, are trained according to actual requirements, so that nonlinear correction performance of the digital predistortion device is improved, and nonlinear distortion problems of the transmitter are solved.

Description

Nonlinear correction method, device and system

Technical Field

The embodiment of the application relates to the technical field of signal processing, in particular to a nonlinear correction method, device and system.

Background

To achieve high capacity and high rate wireless communications to meet the explosive growth of user demands, communication systems provide increasingly higher signal bandwidths and more complex modulation schemes. However, this will result in a communication signal having a higher peak-to-average power ratio (PAPR), simply referred to as peak-to-average power ratio.

Taking the orthogonal frequency division multiplexing (orthogonal frequency division multiplexing, OFDM) communication technique shown in fig. 1 as an example, since an OFDM symbol is formed of a plurality of independently modulated subcarrier signals (as shown in fig. 1

) When the phases of the subcarriers are the same or similar, the superimposed signal is modulated by the same initial phase signal, so that a larger instantaneous power peak value is generated, and a higher PAPR is further brought.

It will be appreciated that the Power Amplifier (PA) is an important component of the transmitter, which naturally has a non-linear characteristic. When the average power of the input signals is the same, the input signals with higher PAPR are more sensitive to the nonlinearity of the PA, and the nonlinear distortion of the signals is easily aggravated. The nonlinear distortion of the signal not only spreads the spectrum and thus causes adjacent channel interference, but also degrades the error vector magnitude (error vector magnitude, EVM) of the transmitted signal, increases the bit error rate of the receiver, and causes the PA operating point to back off, thereby reducing the efficiency of the transmitter. Therefore, high PAPR has become a major technical impediment to high capacity, high rate communication systems.

Disclosure of Invention

The application provides a nonlinear correction method, device and system, which can improve nonlinear correction performance of a predistortion device.

In order to achieve the above purpose, the embodiment of the application adopts the following technical scheme:

in a first aspect, a method of nonlinear correction is provided, the method comprising: the first signal is subjected to preset processing to obtain a second signal; generating an orthogonal regression matrix based on the first signal; constructing and obtaining a forward modeling formula of a Power Amplifier (PA) according to the orthogonal regression matrix; generating an orthogonal basis function according to the PA forward modeling formula; obtaining parameters of the PA model by integrating the product of the orthogonal basis function and the second signal; and adjusting nonlinear correction parameters according to the parameters of the PA model.

According to the scheme provided by the first aspect, the prediction of the power amplifier output under the condition of full sampling is realized by generating the orthogonal regression matrix, generating the orthogonal basis function, correlating the output signal with the parameters of the power amplifier model according to the generated orthogonal basis function, sampling the output signal, and further, the nonlinear correction parameters such as the predistortion parameters are trained according to actual requirements, so that the nonlinear correction performance of the digital predistortion device is improved, and the nonlinear distortion problem of the transmitter is solved.

In one possible implementation, the nonlinear correction parameter is a digital pre-distortion (DPD) parameter. As an example, the nonlinear correction method provided in the present application may be implemented based on a DPD device, where the core of the nonlinear correction is to perform DPD parameter training and adjustment.

In one possible implementation manner, the obtaining PA model parameters by integrating the product of the orthogonal basis functions and the second signal includes: multiplying the orthogonal basis function with an I-path signal and a Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product; and integrating the I path product and the Q path product according to a preset period to obtain parameters of the PA model. By performing the processing of the I-path signal and the Q-path signal, the extraction of each parameter of the PA model can be realized, so that the power amplifier output estimation under the condition of full sampling can be performed later, and further the training DPD parameters can be output according to the estimated power amplifier under the condition of full sampling.

In one possible implementation, the preset period is T _int ，T _int The method meets the following conditions: t (T) _int ≥N _int Ts; wherein N is _int Is of a preset value, N _int Ts is the sampling period in case of full sampling, which is related to the number of PA-model parameters and/or the performance of the transmitter. By implementing the extraction of each parameter of the PA model according to a preset period related to the number of PA model parameters and/or the performance of the transmitter, the extracted PA model parameters can be provided with a reference price, thereby being beneficial to training of DPD parameters.

In one possible implementation manner, the integrating the I-path product and the Q-path product according to the preset period to obtain PA model parameters includes: integrating the I path product and the Q path product at a first moment to obtain a first PA model parameter, wherein the first moment meets a first condition; and integrating the I-path product and the Q-path product at a second moment to acquire a second PA model parameter, wherein the second moment meets a second condition. Exemplary, the first time satisfies (2 k-2) T _int ≤t＜(2k-1)T _int A first PA-model parameter, such as the real part of the PA-model parameter; the second time satisfies (2 k-1) T _int ≤t＜2kT _int A second PA-model parameter, such as the real part of the PA-model parameter. The extraction of each parameter of the PA model can be realized in a time division mode.

In one possible implementation manner, the first signal is a signal of a preset frequency band in the input signal. As an example, the method provided in the present application supports frequency-division extraction of PA model parameters to reduce training time of DPD parameters.

In one possible implementation manner, the method further includes: and carrying out nonlinear correction parameter adjustment on signals of all frequency bands in the input signal by adopting the same method as the first signal. As an example, the method provided in the present application supports frequency-division extraction of PA model parameters to reduce training time of DPD parameters.

In one possible implementation manner, the method further includes: and when the nonlinear correction parameters are stable, carrying out nonlinear correction on the signals before signal transmission according to the adjusted nonlinear correction parameters. By the method, the sampling rate and the sampling time can be comprehensively considered when nonlinear correction is carried out. In addition, the method can also reduce the ADC cost during nonlinear correction.

In one possible implementation manner, the multiplying the orthogonal basis function with the I-path signal and the Q-path signal of the second signal to obtain the I-path product and the Q-path product respectively includes: and synchronously multiplying the orthogonal base function with the I-path signal and the Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product. By way of example, the synchronization of the I-path signal and the Q-path signal may be achieved by a delay.

In one possible implementation manner, the generating an orthogonal regression matrix based on the first signal includes: generating a regression matrix based on the first signal; and carrying out orthogonal transformation on the regression matrix to obtain an orthogonal regression matrix.

In a possible implementation manner, the performing a preset process on the first signal to obtain a second signal includes: and respectively carrying out digital predistortion, digital-to-analog conversion, quadrature modulation and power amplification on the first signal to obtain a second signal.

In a second aspect, there is provided a nonlinear correction apparatus comprising: the transmitting module is used for carrying out preset processing on the first signal to obtain a second signal; the base function generation module is used for: generating an orthogonal regression matrix based on the first signal; constructing and obtaining a forward modeling formula of a Power Amplifier (PA) according to the orthogonal regression matrix; generating an orthogonal basis function according to the PA forward modeling formula; the integral analog-digital conversion module is used for obtaining parameters of the PA model by integrating the product of the orthogonal base function and the second signal; and the predistortion module is used for adjusting nonlinear correction parameters according to the parameters of the PA model.

By way of example, the transmit module may include a digital-to-analog converter (DAC) and a PA.

Illustratively, an integrating analog-to-digital conversion module such as an integrating DAC.

According to the scheme provided by the second aspect, the output signal is correlated with the parameters of the power amplifier model according to the generated orthogonal basis function, the output signal is sampled to realize the extraction of the parameters of the power amplifier model, so that the prediction of the power amplifier output under the condition of full sampling is realized, and further, the nonlinear correction parameters, such as predistortion parameters, are trained according to actual requirements, so that the nonlinear correction performance of the digital predistortion device is improved, and the nonlinear distortion problem of a transmitter is solved.

In one possible implementation, the nonlinear correction parameter is a DPD parameter. As an example, the nonlinear correction method provided in the present application may be implemented based on a DPD device, where the core of the nonlinear correction is to perform DPD parameter training and adjustment.

In one possible implementation manner, the integrating analog-digital conversion module is specifically configured to: multiplying the orthogonal basis function with an I-path signal and a Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product; and integrating the I path product and the Q path product according to a preset period to obtain parameters of the PA model. By performing the processing of the I-path signal and the Q-path signal, the extraction of each parameter of the PA model can be realized, so that the power amplifier output estimation under the condition of full sampling can be performed later, and further the training DPD parameters can be output according to the estimated power amplifier under the condition of full sampling.

In one possible implementation manner, the integrating analog-digital conversion module is specifically configured to: integrating the I path product and the Q path product at a first moment to obtain a first PA model parameter, wherein the first moment meets a first condition; and integrating the I-path product and the Q-path product at a second moment to acquire a second PA model parameter, wherein the second moment meets a second condition. Exemplary, the first time satisfies (2 k-2) T _int ≤t＜(2k-1)T _int First PA model parametersSuch as the real part of PA model parameters; the second time satisfies (2 k-1) T _int ≤t＜2kT _int A second PA-model parameter, such as the real part of the PA-model parameter. The extraction of each parameter of the PA model can be realized in a time division mode.

In one possible implementation, the predistortion module is further configured to: and carrying out nonlinear correction parameter adjustment on signals of all frequency bands in the input signal by adopting the same method as the first signal. As an example, the method provided in the present application supports frequency-division extraction of PA model parameters to reduce training time of DPD parameters.

In one possible implementation, the predistortion module is further configured to: and when the nonlinear correction parameters are stable, carrying out nonlinear correction on the signals before signal transmission according to the adjusted nonlinear correction parameters. By the method, the sampling rate and the sampling time can be comprehensively considered when nonlinear correction is carried out. In addition, the method can also reduce the ADC cost during nonlinear correction.

In one possible implementation manner, the integrating analog-digital conversion module is specifically configured to: and synchronously multiplying the orthogonal base function with the I-path signal and the Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product. By way of example, the synchronization of the I-path signal and the Q-path signal may be achieved by a delay.

In one possible implementation manner, the above-mentioned basis function generation module is specifically configured to: generating a regression matrix based on the first signal; and carrying out orthogonal transformation on the regression matrix to obtain an orthogonal regression matrix.

In one possible implementation manner, the transmitting module is specifically configured to: and respectively carrying out digital predistortion, digital-to-analog conversion, quadrature modulation and power amplification on the first signal to obtain a second signal.

In a third aspect, a computer readable storage medium is provided, on which computer program code is stored which, when executed by a processor, implements a method as in any one of the possible implementations of the first aspect.

In a fourth aspect, a chip system is provided, the chip system comprising a processor, a memory, the memory having computer program code stored therein; the computer program code, when executed by the processor, implements a method as in any one of the possible implementations of the first aspect. The chip system may be formed of a chip or may include a chip and other discrete devices.

In a fifth aspect, a computer program product is provided which, when run on a computer, causes the method as in any one of the possible implementations of the first aspect to be implemented.

Drawings

Fig. 1 is a schematic diagram of a communication process of an OFDM communication technology according to an embodiment of the present application;

fig. 2 is a time domain waveform diagram of an OFDM signal according to an embodiment of the present application;

Fig. 3 is a schematic diagram of nonlinear characteristics of a Power Amplifier (PA) according to an embodiment of the present application;

fig. 4 is a schematic diagram illustrating an operation of a transmitter according to an embodiment of the present application;

fig. 5 is a second schematic diagram of a working process of the transmitter according to the embodiment of the present application;

fig. 6 is a schematic diagram of a third working process of the transmitter according to the embodiment of the present application;

fig. 7 is an exemplary diagram of a communication scenario provided in an embodiment of the present application;

fig. 8 is a schematic hardware structure of a transmitting end/receiving end according to an embodiment of the present application;

fig. 9 is a schematic diagram of a working process of the transmitter according to the embodiment of the present application;

fig. 10 is a schematic diagram of a power amplifier model parameter sampling process according to an embodiment of the present application;

FIG. 11 is a graph illustrating performance parameters corresponding to different times of undersampling according to an embodiment of the present application;

fig. 12 is a schematic diagram of a working process of a transmitter according to an embodiment of the present application;

FIG. 13 is a diagram of an exemplary bandwidth of a band-limited filter according to an embodiment of the present application;

fig. 14 is a schematic diagram of another power amplifier model parameter sampling process according to an embodiment of the present application;

fig. 15 is a schematic diagram sixth working procedure of the transmitter according to the embodiment of the present application;

Fig. 16 is a schematic diagram seventh of an operation procedure of the transmitter according to the embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Wherein, in the description of the embodiments of the present application, "/" means or is meant unless otherwise indicated, for example, a/B may represent a or B; "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, in the description of the embodiments of the present application, "plurality" means two or more than two.

The terms "first" and "second" are used below for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

For ease of understanding, several terms referred to in this application are briefly described below.

1. Orthogonal Frequency Division Multiplexing (OFDM)

The main idea of OFDM is that the transmitting end divides the channel into several orthogonal sub-channels, converts the high-speed data signal into parallel multiple low-speed sub-data streams (as shown in fig. 1

) Transmission occurs over a plurality of sub-channels. Correspondingly, as shown in fig. 1, the receiving end may analyze and acquire a plurality of low-speed sub-data streams by adopting a related technology, so as to reduce mutual interference between sub-channels. Since the signal bandwidth on each subchannel is less than the associated bandwidth of the channel, the signal bandwidth on each subchannel can be seen as flat fading, and thus, inter-symbol interference can be eliminated. Also, since the bandwidth of each sub-channel is only a small fraction of the original channel bandwidth, channel equalization becomes relatively easy.

2. Peak-to-average ratio (PAPR)

PAPR is a measurement parameter for a signal. For example, PAPR, a measure of the dynamic range of the input amplitude of a wireless transmitter, may be used as one of the metrics for measuring the performance of the transmitter. In general, PAPR is used to characterize the ratio of the amplitude of a signal to an effective value, such as Root Mean Square (RMS).

Taking OFDM communication technology as an example, the definition of PAPR is as follows:

where x (T) represents an OFDM signal and T represents an accumulation time period. Referring to fig. 2, fig. 2 shows a schematic diagram of an OFDM signal. Wherein, OFDM shown in figure 1 _peak I.e. max _0≤t＜T x(t)，OFDM _mean I.e. mean _0≤t＜T x(t)。

3. Nonlinear distortion

The output signal is non-linearly distorted relative to the input signal, thereby causing unwanted interference signals that affect the correct transmission and reception of information, a phenomenon known as non-linear distortion.

Illustratively, a Power Amplifier (PA) has the characteristic of nonlinear distortion, i.e., the PA has nonlinear characteristics. For example, for a PA, the power level is set to a predetermined input power range (P as shown in fig. 3 _in1 Range), the input signal is linear with the input signal. As shown in fig. 3, the output power P _out1 And input power P _in1 In a linear relationship. However, when the input power is greater than the preset threshold (P as shown in FIG. 3 _in2 ) The output power gradually exhibits nonlinearity. As shown in fig. 3, the output power P _out2 And input power P' _in2 In a nonlinear relationship, deviate from the value P in an ideal case _in2 。

Since a Power Amplifier (PA) is an important component of a transmitter, the transmitter has a nonlinear characteristic. To correct the nonlinearity of a transmitter, the efficiency and communication quality of the transmitter are improved. As an implementation, the nonlinearity of the transmitter may be corrected by introducing digital pre-distortion (DPD) techniques. Wherein, the principle of DPD is: by providing a non-linear characteristic comparable to, but functionally opposite to, the distortion characteristic of the Power Amplifier (PA), the predistortion element is enabled to correct the non-linearity of the Power Amplifier (PA) such that the integrated processing result of the input signal satisfies a linear relation with the input signal.

As an example, the nonlinearity of the transmitter may be corrected by cascading a predistortion element (predistor) with a Power Amplifier (PA). Wherein the predistortion element (predistor) may provide a non-linear characteristic comparable to the distortion characteristic of the Power Amplifier (PA), but of opposite function, such that the predistortion element may correct the non-linearity of the Power Amplifier (PA). Referring to fig. 4, fig. 4 is a schematic diagram illustrating an operation procedure of a transmitter according to an embodiment of the present application. Where x (t) is an input signal, z (t) is an output signal obtained by predistortion of DPD, and y (t) is an output signal obtained by power amplification of the predistorted signal, as shown in fig. 4. As shown in fig. 4, the output signal y (t) and the input signal x (t) satisfy a linear relationship through the integrated processing of the predistortion element and the Power Amplifier (PA).

As another example, nonlinearities within a certain bandwidth range may be corrected by cascading a band-limited filter (e.g., a low-pass filter) with a Power Amplifier (PA) that is integrated with a nonlinearity correction function. Referring to fig. 5, fig. 5 is a schematic diagram illustrating an operation of another transmitter according to an embodiment of the present application. Where x (n) is an input signal and y (n) is an output signal after power amplification as shown in FIG. 5 ，y _BL And (n) is an output signal after the band-limited filtering DPD. As shown in fig. 5, the nonlinearity of the signal in the bandwidth range of the band-limited filter can be corrected by the processing of the band-limited filter.

As another example, undersampled predistortion may be based on random demodulation. As shown in fig. 6, a pseudo-random signal of

value

0 or 1 may be generated with a period of sampling interval. After the input signal x (t) is subjected to DPD predistortion, digital-to-analog converter (DAC) digital-to-analog conversion, and power amplification of the Power Amplifier (PA) output signal y (t), random sampling may be performed based on the pseudo-random signal to obtain a random sampled signal y _R (t) and obtaining an output signal y' (t) through a band-limiting filter and analog-to-digital conversion. Further, based on the method shown in fig. 6, the predistortion related parameters can be further adjusted according to the signal after predistortion and the output signal y' (t), so as to further improve the nonlinear correction performance of DPD.

However, conventional nonlinear correction methods have more or less problems. For example, based on the nonlinear correction method shown in fig. 5 described above, nonlinear correction of signals outside the bandwidth range of the band-limited filter cannot be achieved. As another example, based on the nonlinear correction method shown in fig. 6, in the case of a DAC with limited bandwidth, the analog pseudo-random signal is severely distorted, and the random demodulation performance is poor.

In order to improve nonlinear correction performance of a transmitter and solve nonlinear distortion problem of the transmitter, the embodiment of the application provides a nonlinear correction method, which can enable each parameter of a power amplifier model to be extracted independently in a forward modeling process of a Power Amplifier (PA) through orthogonalization of a regression matrix in an algorithm level so as to realize adaptive setting and adjustment of parameters of the power amplifier model. In some embodiments, the nonlinear correction method provided by the embodiments of the present application may further extract each parameter of the power amplifier model by introducing a multiplier and an integrator in a time-division manner, so as to implement setting and adjustment supporting undersampling by an infinite multiple.

In an embodiment of the present application, a communication system for performing signal transmission may include a transmitting end, a receiving end, and a communication channel. Wherein the communication channel is a wireless channel or a wired channel, and embodiments of the present application are not limited. Wherein the wireless channel is such as the medium of atmosphere, vacuum, water, etc. for wireless transmission. Wired channels such as optical fiber, copper wire, etc. media for transporting signals.

In this embodiment of the present application, the sending end and the receiving end may be network devices or terminal devices. For example, the transmitting end is a network device, and the receiving end is a terminal device. As another example, the transmitting end is a terminal device and the receiving end is a network device. As another example, both the transmitting end and the receiving end are network devices. As another example, both the transmitting end and the receiving end are terminal devices. The specific functions and structures of the transmitting end and the receiving end are not limited in this application.

Illustratively, a network device such as AN Access Network (AN) or a radio access network (radio access network, RAN). For example, the AN/RAN may be a base station of various forms, such as: macro base stations, micro base stations (also referred to as "small stations"), distributed unit-control units (DU-CUs), and the like. In addition, the base station may be a wireless controller in a cloud wireless access network (cloud radio access network, CRAN) scenario, or a relay station, an access point, an in-vehicle device, a wearable device, or a network device in a future evolved public land mobile network (public land mobile network, PLMN) network, etc. The AN/RAN may also be a broadband network traffic gateway (broadband network gateway, BNG), AN aggregation switch, a non-3 GPP access device, etc. The embodiment of the application does not limit the specific form and structure of the AN/RAN. For example, in systems employing different radio access technologies, the names of base station capable devices may vary. For example, the base station may be an evolved universal terrestrial radio access network (evolved universal terrestrial radio access network, E-UTRAN) device in LTE, such as an evolved node B (eNB or E-NodeB), or a next generation radio access network (next generation radio access network, NG-RAN) device in a 5G system, such as a gNB, or the like.

By way of example, terminal devices such as desktop devices, laptop devices, handheld devices, wearable devices, smart home devices, computing devices, and vehicle-mounted devices, etc. having communication capabilities. For example, netbooks, tablet computers, smartwatches, personal computers (personal computer, PCs), ultra-mobile personal computers (ultra-mobile personal computer, UMPC), smartcameras, netbooks, personal digital assistants (personal digital assistant, PDA), cellular phones, cordless phones, session initiation protocol (session initiation protocol, SIP) phones, wireless local loop (wireless local loop, WLL) stations, portable multimedia player (portable multimedia player, PMP), (augmented reality, AR)/Virtual Reality (VR) devices, wireless devices on board an aircraft, wireless devices on robots, wireless devices in industrial control, wireless devices in telemedicine, wireless devices in Smart grids, wireless devices in Smart cities (Smart City), wireless devices in Smart home (Smart home), and the like. The embodiment of the application does not limit the specific type and structure of the terminal equipment.

It should be noted that the terms "system" and "network" used in the embodiments of the present application may be used interchangeably.

In the embodiment of the present application, terms such as "terminal equipment", "Mobile Station (MS)", "user terminal (UE)", "User Equipment (UE)", and "terminal" are used interchangeably. Terminal devices are sometimes referred to by those skilled in the art as subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless terminals, remote terminals, handsets, user agents, mobile clients, or several other suitable terms.

In addition, the base station in the embodiment of the application may also be replaced by a user terminal. For example, the embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a terminal device is replaced with communication between a plurality of user terminals (D2D). At this time, the function of the base station may be regarded as the function of the user terminal. Further, words such as "up" and "down" may be replaced with "side". For example, the uplink channel may be replaced by a side channel. Also, the user terminal in the embodiment of the present application may be replaced by a base station. In this case, the function of the user terminal may be regarded as a function of the base station.

In the embodiment of the application, the transmitting end comprises a transmitter, and the receiving end comprises a receiver. Wherein, the transmitter in the sender is used for supporting the sender to transmit signals, and the receiver in the receiver is used for supporting the receiver to receive signals.

In some possible configurations, the transmitting end may further include a receiver, and the receiving end may further include a transmitter. Wherein, the receiver in the sender is used for supporting the sender to receive signals, and the transmitter in the receiver is used for supporting the receiver to transmit signals.

As an example, please refer to fig. 7, fig. 7 shows a schematic diagram of a communication scenario provided in an embodiment of the present application. Wherein the communication system in the communication scenario shown in fig. 7 comprises a transmitter located in a transmitting end, a receiver located in a receiving end and a communication channel (wherein the transmitting end and the receiving end are not shown in fig. 7).

As shown in fig. 7, the transmitter includes a baseband processing unit 710, DPD 720, DAC 730, in-transmit rf 740, and PA 750. The receiver includes a receiving radio 760, an analog-to-digital converter (ADC) 770, and a baseband processing unit 780.

The baseband processing unit 710 in the transmitter is mainly used for modulating, framing, filtering, predistortion correction, and the like of information. DPD 720 is mainly used for non-linearization pre-correction of PA 750. DAC 730 is primarily used for digital-to-analog conversion. The transmitting rf 740 is mainly used for modulating a baseband signal into an rf signal, filtering the signal, and the like. The PA 750 is mainly used for power amplifying signals.

The in-receive radio 760 in the receiver is primarily used to down-convert the received radio frequency signal to a low frequency or baseband. The ADC 770 is mainly used for analog-to-digital conversion. The baseband processing unit 780 is mainly used for recovering baseband signals, such as synchronization, equalization, etc.

In some embodiments, the transmitter shown in fig. 7 may further include an encoding unit, and the receiver may further include a decoding unit. The coding unit is mainly used for correcting, coding, interleaving and the like of information on the signals. The decoding unit is mainly used for decrypting, decoding and the like signals.

It should be noted that fig. 7 is only an example of a communication architecture, and the present application is not limited to a specific communication architecture of the communication system. For example, in embodiments of the present application, the communication system may also include other units or modules. As another example, in embodiments of the present application, the transmitter or receiver may also include more communication devices than in fig. 7 or may not include a certain communication device in fig. 7.

As an example, please refer to fig. 8, fig. 8 illustrates a hardware configuration diagram of a transmitting end/receiving end by taking a terminal device as an example. As shown in fig. 8, in some embodiments, the structure of the transmitting/receiving end may be as shown in fig. 8, and the transmitting/receiving end may include: processor 810, external memory interface 820, internal memory 821, universal serial bus (universal serial bus, USB) interface 830, charge management module 840, power management module 841, battery 842, antenna 1, antenna 2, mobile communication module 850, wireless communication module 860, audio module 870, speaker 870A, receiver 870B, microphone 870C, ear-piece interface 870D, sensor module 880, keys 890, motor 891, indicator 892, camera 893, display 894, and subscriber identity module (subscriber identification module, SIM) card interface 895, among others. The sensor module 880 may include, among other things, a pressure sensor, a gyroscope sensor, a barometric sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, etc.

It is to be understood that the configuration illustrated in this embodiment does not constitute a specific limitation on the transmitting side/receiving side. In other embodiments, the transmitting/receiving end may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 810 may include one or more processing units, such as: the processor 810 may include an application processor (application processor, AP), a Modem, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The wireless communication function of the transmitting/receiving end can be implemented by the antenna 1, the antenna 2, the mobile communication module 850, the wireless communication module 860, a modem, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the transmitting/receiving end may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas.

The mobile communication module 850 may provide a solution including 2G/3G/4G/5G/6G wireless communication applied on a transmitting/receiving end.

In the embodiment of the present application, the antenna 1 and the antenna 2 may be used for a transmitting end/receiving end to transmit signals or receive signals. For example, antenna 1 and/or antenna 2 may comprise a transmitter and/or receiver as shown in fig. 7.

The wireless communication module 860 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), etc., applied on the transmitting/receiving end. The wireless communication module 860 may be one or more devices that integrate at least one communication processing module. The wireless communication module 860 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 810. The wireless communication module 860 may also receive signals to be transmitted from the processor 810, frequency modulate them, amplify them, and convert them to electromagnetic waves for radiation via the antenna 2.

The external memory interface 820 may be used to connect an external memory card, such as a Micro SD card, to enable expansion of the memory capabilities of the transmitting/receiving ends.

The internal memory 821 may be used to store computer-executable program code that includes instructions. The processor 810 executes various functional applications of the transmitting side/receiving side and data processing by executing instructions stored in the internal memory 821.

In addition, an operating system, such as iOS operating system, android operating system, windows operating system, etc., is run on the above components. An operating application may be installed on the operating system. In other embodiments, there may be multiple operating systems running within the sender/receiver.

It should be understood that the hardware modules included in the transmitting/receiving end shown in fig. 8 are only exemplarily described, and the specific structure of the transmitting/receiving end is not limited. In fact, the transmitting end/receiving end provided in the embodiment of the present application may further include other hardware modules having an interaction relationship with the hardware modules illustrated in the drawings, which is not limited herein specifically. For example, the transmitting/receiving end may further include a flash, a micro-projector, etc. For another example, if the transmitting/receiving end is a PC, the transmitting/receiving end may further include a keyboard, a mouse, and the like.

The following describes a nonlinear correction method provided in the embodiments of the present application with reference to the accompanying drawings.

In order to support the nonlinear correction of the transmitter during signal transmission, the scheme provided by the embodiment of the application introduces a coefficient (also called parameter) based perceived undersampled digital predistortion device (namely a nonlinear correction device, hereinafter called digital predistortion device) into the transmitter. As an example, as shown in fig. 9, the digital predistortion device includes a base function generating module 900, a DPD 910, a DAC 920-1, a DAC 920-2, an integrating ADC 930, a quadrature modulator (quadrature modulator, QMod) 940, a quadrature demodulator (quadrature demodulator, QDMod) 950, a PA 960, an auxiliary circuit (a multiplier 970-1 and a multiplier 970-2 as shown in fig. 9), and a coupler 980.

In some embodiments, to facilitate coefficient extraction and training, as shown in fig. 9, the digital predistortion device may further include an attenuator 990 for preset attenuation of the coupled signal over a specified frequency range.

The nonlinear correction method provided by the embodiment of the application can be realized based on the digital predistortion device shown in fig. 9. The nonlinear correction method provided by the embodiment of the application mainly comprises the following four steps:

Step 1: generating a regression matrix, and carrying out orthogonalization processing on the regression matrix in a digital domain.

The orthogonalization processing is carried out on the regression matrix in the digital domain, so that the output signal can be conveniently related to the parameters of the power amplifier model. Taking the digital predistortion device shown in fig. 9 as an example, the above step 1 may be responsible for DPD 910.

Step 2: orthogonal basis functions are generated in the analog domain and multiplied with the output signal by an analog multiplier.

Wherein the orthogonal basis functions are used to correlate the output signal with parameters of the power amplifier model.

Taking the digital predistortion device shown in fig. 9 as an example, the above step 2 may be responsible for the basis function generating module 900. The output signal is a signal subjected to quadrature demodulation by QDMod 950.

Step 3: the product of the analog multiplier output is processed by an analog integrator.

Step 4: and extracting the power amplifier model parameters output by the analog integrator according to actual requirements so as to realize undersampling of the power amplifier model parameters.

Taking the example that the input signal is a digital domain baseband input signal x (n), as shown in FIG. 9, the input signal x (n) comprises an I (in-phase) path signal x _I (n) and Q (quadrature) path signals x _Q (n). As shown in fig. 9, a nonlinear correction method provided in an embodiment of the present application may include the following S901-S914:

S901: DPD 910 digitally predistorts input signal x (n) based on a first predistortion parameter, outputting x _I ' (n) and x _Q ′(n)。

For example, a first predistortion parameter such as an initial predistortion parameter. For example, the initial predistortion parameters may be preset in the DPD 910.

As another example, the first predistortion parameters are DPD parameters obtained by previous training. For example, training of DPD parameters may be achieved based on the nonlinear correction method provided in the embodiments of the present application when signal transmission is performed last time.

S902: DAC 920-1 vs x _I ' (n) and x _Q ' (n) digital-to-analog conversion, output x _I (t) and x _Q (t)。

S903: QMod 940 pair x _I (t) and x _Q (t) quadrature modulated, output x' (t), and fed into PA960.

Wherein the quadrature modulation comprises up-conversion.

S904: PA960 amplifies the power of x' (t) and outputs y (t).

S905: coupler 980 couples output signal y (t), output y, of PA960 ₁ (t)。

S906: attenuator 990 pair y ₁ (t) performing preset attenuation to output y ₂ (t)。

S907: QDMod 950 pair y ₂ (t) quadrature demodulation, output y ₃ (t). Wherein y is ₃ (t) includes an I-way signal y _I (t) and Q-way signal y _Q (t)。

Wherein the quadrature demodulation includes quadrature down-conversion.

S908: the basis function generation module 900 generates a regression matrix based on the input signal x (n)

Wherein,,

the following forward modeling equation 1 is satisfied:

wherein, in the above formula 1,

p is the order of the regression matrix and M is related to the historical input signal. />

The method meets the following conditions:

in the above formula 1, y= [ y (0), …, y (N), …, y (N-1)] ^T . y (n) satisfies:

in the above formula 1, b= [ b ] _1,0 ,…,b _p,m ,…,b _P,M ] ^T . Wherein b _p,m Is a regression matrix parameter.

S909: the basis function generation module 900 pairs regression matrices

And performing orthogonal transformation to obtain an orthogonal regression matrix psi.

Wherein,,

u is the orthogonal projection matrix formed by the eigenvectors. Psi satisfies psi ^H Ψ＝Σ。

Illustratively, DPD 910 may pair the regression matrix

Performing eigenvalue decomposition as shown in the following formula 2 to obtain U and Σ:

in the above formula 2, Σ is a eigenvalue matrix, Σ=diag (λ1 ₁ ,…,λ _k ,…,λ _K ). K is the number of coefficients of the DPD model, k=p× (m+1).

S910: the basis function generating module 900 constructs and obtains a power amplifier forward modeling formula according to the orthogonal regression matrix ψ.

The forward modeling formula of the power amplifier is shown as the following formula 3:

y=ψc. (equation 3)

Wherein c is a power amplifier model parameter, and c satisfies b=uc.

It will be appreciated that based on the forward modeling formula (i.e. formula 3), the power amplifier model parameter c= (ψ) can be determined using the least squares method ^H Ψ) ^-1 Ψ ^H And y. Wherein c= [ c ] ₁ ,…,c _k ,…,c _K ]。

Based on the above equation 3, it can be calculated:

c _k ＝λ _k ^-1 Ψ _k ^H y

＝λ _k ^-1 (Ψ _I,k ^H y _I -Ψ _Q,k ^H y _Q )+jλ _k ^-1 (Ψ _I,k ^H y _I +Ψ _Q,k ^H y _Q )。

wherein ψ is _k The subscripts I and or Q represent the real and imaginary parts of the signal, respectively, for the time series of the kth basis function of the orthogonal regression matrix.

Based on the above c _k The calculation formula of (2) can be obtained: the power amplifier model parameter c _k The real part of (c) _I,k The imaginary part is c _Q,k . Wherein:

wherein T is _int The integration time corresponding to the N sampling points.

S911: the basis function generation module 900 generates a basis function matrix z (n) based on the forward modeling formula of the power amplifier. z (n) includes I way z _I (n) and Q way z _Q (n)。

S912: DAC 920-2 vs. z _I (n) and z _Q (n) digital-to-analog conversion, output z _I (t) and z _Q (t)。

Wherein z is _I (t)＝λ _k ^-1 ψ _I,k (t),z _Q (t)＝-λ _k ^-1 ψ _Q,k (t)。

S913: multiplier 970-1 will z _I (t) and QDMod 950 output signal y _I (t) multiplying; multiplier 970-2 will z _Q (t) and QDMod 950 output signal y _Q (t) multiplying.

As shown in FIG. 10, after the processing by multiplier 970-1, the output signal of multiplier 970-1 is z _I (t)y _I (t); after processing by multiplier 970-2, the output signal of multiplier 970-2 is z _Q (t)y _Q (t). Wherein z is _I (t)y _I (t)＝λ _k ^-1 ψ _I,k (t)y _I (t)，z _Q (t)y _Q (t)＝λ _k ^-1 ψ _Q,k (t)y _Q (t)。

S914: integrating ADC 930 integrates the output signals of multipliers 970-1 and 970-2 according to a preset period to extract the power amplifier model parameters.

Wherein the predetermined period is T _int 。T _int The method meets the following conditions:

T _int ≥N _int Ts。

wherein N is _int Is of a preset value, N _int Is related to the number of power amplifier model parameters and/or the performance of the transmitter. Ts is the sampling period in case of full sampling.

As shown in fig. 10, after the integrating ADC 930 processes, the output signal of the integrating ADC 930 includes an I-path signal and a Q-path signal. Wherein. The I path signal is

Q-way signal is->

It can be understood that the I-path signal and the Q-path signal outputted by the integrating ADC 930 are superimposed

I.e. power amplifier model parameter c _k The real part x of (2) _I，k . Difference between I-path signal and Q-path signal of output of integrating ADC 930>

I.e. power amplifier model parameter c _k Imaginary part c of (2) _Q，k . Thus, integrating ADC 930 integrates the output signals of multipliers 970-1 and 970-2, which is equivalent to sampling the power amplifier model parameters.

Wherein based on a preset period T _int Can determine z _I (t) and z _Q (t) is specifically as follows:

based on z _I (t) and z _Q As can be seen from the calculation formula of (T), when (2 k-2) T _int ≤t＜(2k-1)T _int When the coefficient sampled by the integrating ADC 930 is the power amplifier model parameter c _k The real part c of (2) _I，k When (2 k-1) T _int ≤t＜2kT _int When the coefficient sampled by the integrating ADC 930 is the power amplifier model parameter c _k Is a virtual part of (c). Therefore, the integrating ADC 930 can extract each parameter of the power amplifier model in a time division manner according to the actual requirement.

Also, in the embodiment of the present application, the integrating ADC 930 is configured according to the preset period T _int The output signals of multiplier 970-1 and multiplier 970-2 are integrated, i.e., only one point is sampled for each integration interval, integrating the coefficient sense sample rate f of ADC 930 _int The method meets the following conditions:

thus, integrating ADC 930 integrates the output signals of multipliers 970-1 and 970-2 to achieve a minimum multiple of N _int Is not yet sampled. Based on this mechanism, integrating ADC 930 may implement undersampling of the power amplifier model parameters according to actual requirements. For example, please refer to fig. 11, fig. 11 shows performance parameters corresponding to undersampling by different multiples. As shown in fig. 11, N _int The larger the sampling rate (Sa/s) the smaller the normalized minimum mean square error (normalized minimum mean square error, NMSE) (in dB) the larger the error vector magnitude (error vector magnitude, EVM) the larger the adjacent channel power ratio (adjacent channel power ratio, ACPR).

It should be noted that fig. 11 is only an example of a simulation performance parameter under a specific condition, and is only used as a reference, and the specific performance parameter that can be achieved by the actual undersampling depends on the situation.

It will be appreciated that typically, the nonlinearity of the PA is time-varying and the corresponding nonlinear coefficient is slow-varying, and thus, in some embodiments, the integrating ADC 930 described above may be a low-speed ADC. By using a low-speed ADC instead of a high-speed ADC, the cost of extracting the parameters of the power amplifier model can be reduced.

After the parameters of the power amplifier model are obtained, further, as shown in fig. 9, the nonlinear correction method provided in the embodiment of the present application can estimate the power amplifier output under the condition of full sampling, and further train DPD parameters according to the estimated power amplifier output under the condition of full sampling.

The power amplifier output y' under the condition of full sampling can be calculated according to the following formula 4:

y '=ψc'. (equation 4)

In the above formula 4, c' is a power amplifier model parameter in the case of full sampling.

As an example, in the nonlinear correction method provided in the embodiment of the present application, DPD parameters may be trained according to the estimated power amplifier output under the full sampling condition based on a conventional structure such as indirect learning or direct learning. The embodiment of the present application is not limited to a specific method, and a specific method and a specific process for training DPD parameters may refer to a conventional technology, which are not described herein.

According to the nonlinear correction method provided by the embodiment of the application, the digital predistortion device based on coefficient (also called parameter) perception is introduced into the transmitter, the regression matrix is generated, orthogonalization processing is carried out on the regression matrix, an orthogonalization basis function is generated, an output signal is obtained, the output signal is associated with the parameters of the power amplification model according to the orthogonalization basis function, the output signal is sampled so as to realize extraction of the parameters of the power amplification model, so that the prediction of the power amplification output under the condition of full sampling is realized, further, the DPD parameters are trained according to actual requirements, the nonlinear correction performance of the digital predistortion device is improved, and the nonlinear distortion problem of the transmitter is solved.

In some embodiments, in order to reduce training time of DPD parameters, in the nonlinear correction method provided in the embodiments of the present application, a forward modeling manner of a frequency-divided power amplifier may also be used.

As an example, the auxiliary circuit frequency division band extraction power amplifier model parameters including integrating ADC 930, multiplier 970-1, multiplier 970-2, multiplier 970-3 and band limiting filter 970-4 shown in fig. 12 may be employed.

For example, in the embodiments of the present application, the output signal may be band-limited for M times. Illustratively, the band limiting filter 970-4 may filter the signal according to a preset frequency band parameter. The preset frequency band parameters include, for example, the bandwidth of the band limiting filter 970-4. For example, the bandwidth of band-limiting filter 970-4, such as fs/M, refers to dividing the full sampling bandwidth fs into M segments. Assuming m=5, the bandwidth of the band-limited filter is fs/5 as shown in fig. 13. Wherein f1=f2=f3=f4=f5=fs/5 shown in fig. 13.

The transfer function of band limiting filter 970-4 is BL (), then the signal ym for band m and the regression matrix

The kth column basis function of (2) satisfies:

wherein,,

corresponding regression matrix->

An orthogonal regression matrix ψ can be obtained after orthogonal transformation _m ：

Further, after the eigenvalue decomposition of the orthogonal regression matrix ψm is performed as shown in the following equation 5, U can be obtained _m Sum sigma _m ：

In the above equation 5, Σ _m Is a eigenvalue matrix corresponding to the frequency band m, sigma _m ＝diag(λ _m，1 ，…，λ _m，k ，…，λ _m，K ). K is the number of coefficients of the DPD model, k=p× (m+1).

Based on the orthogonal regression matrix ψm corresponding to the frequency band m, a power amplifier forward modeling formula (e.g., the following formula 6) of the frequency band m can be constructed.

y _m ＝Ψ _m c _m . (equation 6)

Further, based on the forward modeling formula (i.e. formula 6) of the power amplifier of the frequency band m, the least square method can be used for determining the power amplifier model parameter c of the frequency band m _m ＝(Ψ _m ^H Ψ _m ) ^-1 Ψ _m ^H y _m . Wherein c _m ＝[c _m，1 ，…，c _m，k ，…，c _m，K ]。c _m The real part c of (2) _I，m，k And imaginary part c _Q，m，k The method comprises the following steps of:

further, the basis function generating module 900 may generate the basis function matrix z of the frequency band m based on the forward modeling formula of the power amplifier of the frequency band m _m (n)。z _m (n) includes I way z _I，m (n) and Q way z _Q，m (n). DAC 920-2 vs. z _I，m (n) and z _Q，m (n) digital-to-analog conversion, can output z _m (t). Wherein z is _m (t) includes a real part z _I，m (t) and imaginary part z _Q，m (t). Wherein:

another method for correcting nonlinearity provided in the embodiment of the present application will be specifically described below with reference to fig. 12.

As shown in S1201 in fig. 12, after S907, multiplier 970-3 pairs signal y ₃ (t) performing frequency shift of-M fs/M, and inputting the frequency-shifted signal to the band-limiting filter 970-4.

Further, as shown in S1202 of fig. 12, the band-limiting filter 970-4 filters the signal according to the preset frequency band setting parameter.

Taking the processing of the signal in the frequency band m as an example, as shown in fig. 12, the output of the band-limiting filter includes an I-path signal y _I，m (t) and Q-way signal y _Q，m (t)。

Further, as shown in S1203 of FIG. 12, multiplier 970-1 will z _I，m (t) and QDMod 950 output signal y _I，m (t) multiplying; multiplier 970-2 will z _Q，m (t) and QDMod 950 output signal y _Q，m (t) multiplying.

Wherein, after being processed by the multiplier 970-1, the output signal of the multiplier 970-1 is z _I，m (t)y _I， m (t); after processing by multiplier 970-2, the output signal of multiplier 970-2 is z _Q，m (t)y _Q，m (t). Wherein z is _I，m (t)y _I，m (t)＝λ _m，k ^-1 ψ _I，m，k (t)y _I，m (t)，z _Q，m (t)y _Q，m (t)＝λ _k，m ^-1 ψ _Q，m，k (t)y _Q，m (t)。

Further, as shown in S1204 in fig. 12, the integrating ADC 930 may be based on the time domain basis function matrix z of the frequency band m according to actual requirements _m (t) integrating the output signals of the multipliers 970-1 and 970-2 according to a preset period to extract the power amplifier model parameter c of the frequency band m _I，m，k And/or c _Q，m，k 。

For example, when [2 (m-1) K+2k-2]T _int ≤t＜[2(m-1)K+2k-1|T _int At the time, sampling is carried out to obtain the power amplifier model parameter c of the frequency band m _I，m，k When [2 (m-1) K+2k-1]T _int ≤t＜[2(m-1)K+2k]T _int At the time, sample toObtaining a power amplifier model parameter c of a frequency band m _Q，m，k . With respect to the specific procedures of S1101 to S1104 described above, reference may be made to fig. 14.

After obtaining the parameters of the power amplifier model of the frequency band m, further, as shown in fig. 12, the nonlinear correction method provided in the embodiment of the present application may estimate the power amplifier output of the frequency band m under the condition of full sampling, and further train DPD parameters according to the estimated power amplifier output of the frequency band m under the condition of full sampling. For example, in the embodiment of the present application, DPD parameters may be trained based on a conventional indirect learning structure or a structure such as direct learning. Wherein, the power amplifier output y of the frequency band m under the condition of full sampling _m ’＝Ψ _m c _m ’。c _m ' is the power amplifier model parameter of the frequency band m under the condition of full sampling.

Or after the parameters of the power amplifier model of the frequency band m are obtained, further, the nonlinear correction method provided by the embodiment of the application can estimate the power amplifier output of the frequency band m under the condition of full sampling. For each frequency band, reference is made to the processing method for frequency band m. Under ideal conditions, the power amplifier output of a plurality of frequency bands is overlapped after M times up-sampling and-mfs/M frequency shift, so that the power amplifier output under the condition of full sampling can be obtained, and further DPD parameters are trained. For example, in the embodiment of the present application, DPD parameters may be trained based on a conventional indirect learning structure or a structure such as direct learning.

In the embodiment of the present application, by dividing the full sampling bandwidth fs into M segments, the bandwidth of the integrating DAC 930 can be reduced by M times, thereby allowing the use of a low-speed ADC instead of a high-speed ADC to reduce the cost of power amplifier model parameter extraction.

In addition, due to c _m The' dimension is a column vector of Kx1, in particular, since forward modeling is performed on different frequency bands, different basis functions are allowed to be adopted, namely K can be expressed as an M-related quantity, namely K (M), for example, the number of coefficients corresponding to the frequency band in the band, such as K (0), can be larger, the number of coefficients corresponding to the frequency band out of the band, particularly the frequency band at the frequency spectrum edge, such as K (M/2), can be smaller, so that power amplifier model parameters can be flexibly selected, and training time of DPD parameters can be reduced.

It can be appreciated that the nonlinear correction method provided in the embodiments of the present application may be implemented based on a band-limited filter, or may be compatible with a non-band-limited scheme. The band limited nonlinear correction method can reduce the digital sampling rate but M times the sampling time compared to the non-band limited nonlinear correction method. In order to comprehensively consider the sampling rate and the sampling time, the embodiment of the application can introduce a DAC mechanism of time division. Illustratively, upon power-up of the digital predistortion device, the method shown in fig. 9 may be selected for nonlinear correction; when the performance of the digital predistortion device is stable, the DPD parameters in a stable state are selected to be adopted for nonlinear correction.

As an example, the time-division DAC mechanism described above may be implemented by a time-division switch (e.g., a signal-selection time-division switch).

Referring to fig. 15, fig. 15 shows a schematic structural diagram of another digital predistortion device. As shown in fig. 15, the digital predistortion device includes a time-division switch 1501 and a DAC 1502. Wherein the time switch 1501 may operate in an operating state 1 or an operating state 2.

When the switch 1501 is operated in the operation state 1, the input of the DAC 1502 is the output of the DPA 910. DAC 1502 may be used for pair x _I ' (n) and x _Q ' (n) digital-to-analog conversion, output x _I (t) and x _Q (t)。

The input of DAC 1502 may also include the output of basis function generation module 900 when switch 1501 is operating in operating state 2. DAC 1502 may be used for pair x _I ' (n) and x _Q ' (n) digital-to-analog conversion, output x _I (t) and x _Q (t); and, to z _I (n) and z _Q (n) digital-to-analog conversion, output z _I (t) and z _Q (t)。

As shown in fig. 15, the time-division switching-based nonlinear correction scheme can also reduce the digital predistortion device by one ADC, and thus the time-division switching-based nonlinear correction scheme can also reduce the cost of nonlinear correction.

In some embodiments, to ensure that multiplier 970-1 and multiplier 970-2 process the I and Q signals synchronously, a delay 1600 may also be introduced by embodiments of the present application, as shown in FIG. 16. The delay 1600 may be an adjustable delay, so that delay parameters (such as delay duration, precision, etc.) of the delay may be adjusted according to actual requirements. The accuracy of the delay is illustratively related to the symbol rate Fs of the current signal, e.g. the accuracy of the delay is 1/(5 xfs).

It should be noted that fig. 15 is only an example of a digital predistortion device structure including a time-division switch, fig. 16 is only an example of a digital predistortion device structure including a delay device, and the nonlinear correction scheme based on the time-division switch provided in the embodiment of the present application may also be adapted to other structures, such as the digital predistortion device with the structure shown in fig. 12. The nonlinear correction scheme based on the delayer provided in the embodiment of the present application may also be adapted to other structures, such as digital predistortion devices with the structures shown in fig. 9 or fig. 12.

It should be understood that various aspects of the embodiments of the present application may be used in reasonable combination, and that the explanation or illustration of the various terms presented in the embodiments may be referred to or explained in the various embodiments without limitation.

It should also be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.

It will be appreciated that the nonlinear correction apparatus, in order to implement the functions of any of the above embodiments, includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The nonlinear correction device may be divided into functional modules, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.

It should be understood that each module in the nonlinear correction apparatus may be implemented in software and/or hardware, which is not particularly limited. In other words, the electronic device is presented in the form of functional modules. A "module" herein may refer to an application specific integrated circuit ASIC, an electronic circuit, a processor and memory that execute one or more software or firmware programs, an integrated logic circuit, and/or other devices that can provide the described functionality.

In an alternative, when data transmission is implemented using software, it may be implemented wholly or partly in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are fully or partially implemented. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line ((digital subscriber line, DSL)), or wireless (e.g., infrared, wireless, microwave, etc.), the computer-readable storage medium may be any available medium that can be accessed by the computer or a data storage device such as a server, data center, etc., that contains an integration of one or more available media, the available media may be magnetic media, (e.g., floppy disk, hard disk, tape), optical media (e.g., digital versatile disk (digital video disk, DVD)), or semiconductor media (e.g., solid State Disk (SSD)), etc.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware or in software instructions executed by a processor. The software instructions may be comprised of corresponding software modules that may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in an electronic device. It is of course possible that the processor and the storage medium are present as separate components in the non-linearity correction device.

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.

Claims

1. A method of non-linearity correction, the method comprising:

the first signal is subjected to preset processing to obtain a second signal;

generating an orthogonal regression matrix based on the first signal;

constructing and obtaining a power amplifier PA forward modeling formula according to the orthogonal regression matrix;

generating an orthogonal basis function according to the PA forward modeling formula;

obtaining parameters of a PA model by integrating the product of the orthogonal basis function and the second signal;

and adjusting nonlinear correction parameters according to the parameters of the PA model.

2. The method of claim 1, wherein the nonlinear correction parameter is a digital predistortion DPD parameter.

3. The method according to claim 1 or 2, wherein said obtaining PA model parameters by integrating the product of said orthogonal basis functions and said second signal comprises:

multiplying the orthogonal basis function with an I-path signal and a Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product;

and integrating the I path product and the Q path product according to a preset period to acquire the parameters of the PA model.

4. A method according to claim 3, wherein the predetermined period is T _int The T is _int The method meets the following conditions:

T _int ≥N _int Ts

wherein the N is _int At a preset value, the N is _int The Ts is the sampling period in case of full sampling, depending on the number of PA-model parameters and/or the performance of the transmitter.

5. The method according to claim 3 or 4, wherein integrating the I-path product and Q-path product according to a preset period to obtain the PA model parameters comprises:

integrating the I path product and the Q path product at a first moment to acquire a first PA model parameter, wherein the first moment meets a first condition;

and integrating the I path product and the Q path product at a second moment to acquire a second PA model parameter, wherein the second moment meets a second condition.

6. The method according to any of claims 1-5, wherein the first signal is a signal of a preset frequency band in the input signal.

7. The method of claim 6, wherein the method further comprises:

and carrying out nonlinear correction parameter adjustment on signals of all frequency bands in the input signal by adopting the same method as the first signal.

8. The method according to any one of claims 1-7, further comprising:

And when the nonlinear correction parameters are stable, carrying out nonlinear correction on the signals before signal transmission according to the adjusted nonlinear correction parameters.

9. The method according to any one of claims 3-8, wherein multiplying the orthogonal basis function with the I-and Q-path signals of the second signal, respectively, to obtain an I-and Q-path product, comprises:

and synchronously multiplying the orthogonal base function with an I path signal and a Q path signal of the second signal respectively to obtain the I path product and the Q path product.

10. The method of any of claims 1-9, wherein the generating an orthogonal regression matrix based on the first signal comprises:

generating a regression matrix based on the first signal;

and carrying out orthogonal transformation on the regression matrix to obtain the orthogonal regression matrix.

11. The method according to any one of claims 1-10, wherein the performing a preset process on the first signal to obtain a second signal includes:

and respectively carrying out digital predistortion, digital-to-analog conversion, quadrature modulation and power amplification on the first signal to obtain the second signal.

12. A non-linearity correction device, the device comprising:

The transmitting module is used for carrying out preset processing on the first signal to obtain a second signal;

a basis function generation module for generating an orthogonal regression matrix based on the first signal; constructing and obtaining a power amplifier PA forward modeling formula according to the orthogonal regression matrix; generating an orthogonal basis function according to the PA forward modeling formula;

the integral analog-digital conversion module is used for obtaining parameters of the PA model by integrating the product of the orthogonal base function and the second signal;

and the predistortion module is used for adjusting nonlinear correction parameters according to the parameters of the PA model.

13. The apparatus of claim 12, wherein the nonlinear correction parameter is a digital predistortion DPD parameter.

14. The apparatus according to claim 12 or 13, wherein the integrating analog-to-digital conversion module is specifically configured to:

multiplying the orthogonal basis function with an I-path signal and a Q-path signal of the second signal respectively to obtain an I-path product and a Q-path product; the method comprises the steps of,

15. The apparatus of claim 14, wherein the predetermined period is T _int The T is _int The method meets the following conditions:

T _int ≥N _int Ts

wherein the N is _int At a preset value, the N is _int In relation to the number of PA-model parameters and/or the performance of the transmitter,and the Ts is a sampling period in the case of full sampling.

16. The apparatus according to claim 14 or 15, wherein the integrating analog-to-digital conversion module is specifically configured to:

17. The apparatus according to any of claims 12-16, wherein the first signal is a signal of a preset frequency band in the input signal.

18. The apparatus of claim 17, wherein the integrating analog-to-digital conversion module is further configured to:

19. The apparatus of any of claims 12-18, wherein the predistortion module is further configured to, when the nonlinear correction parameter is stable:

And carrying out signal nonlinear correction before signal transmission according to the adjusted nonlinear correction parameters.

20. The apparatus according to any one of claims 16-19, wherein the integrating analog-to-digital conversion module is specifically configured to:

21. The apparatus according to any of the claims 12-20, wherein the basis function generating module is specifically configured to:

generating a regression matrix based on the first signal;

22. The apparatus according to any one of claims 12-21, wherein the transmitting module is specifically configured to:

23. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program code which, when executed by a processing circuit, implements the method according to any of claims 1-11.

24. A chip system, comprising a processing circuit, a storage medium having computer program code stored therein; the computer program code implementing the method according to any of claims 1-11 when executed by the processing circuit.

25. A computer program product for running on a computer to implement the method of any one of claims 1-11.