Digital pre-distortion method and device, storage medium and computer program product
By designing a non-uniform sampling period and interval, the hardware implementation difficulties and aliasing problems in digital predistortion technology are solved. This allows for a reduction in the sampling rate without sacrificing signal quality, thereby reducing hardware costs and energy consumption, making it suitable for high-speed signal processing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-02
- Publication Date
- 2026-03-10
AI Technical Summary
In existing digital predistortion techniques, high sampling rates make hardware implementation difficult, while low sampling rates lead to aliasing problems, making it difficult to reduce the sampling rate without sacrificing signal quality.
The signal is sampled using a non-uniform sampling period and a non-uniform sampling interval. The fitness value of the frequency domain signal is determined by a non-uniform Fourier transform. Digital predistortion processing is performed under preset conditions. The non-uniform sampling period and interval are designed to meet the system requirements.
It significantly reduces the difficulty of hardware implementation, reduces the sampling rate, reduces the need for anti-aliasing filters, and reduces system power consumption and cost, making it suitable for high-speed signal processing.
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Figure CN121644290A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless communication, and more particularly to a digital predistortion method and apparatus, storage medium, and computer program product. Background Technology
[0002] Digital predistortion is a widely used algorithm in wireless communication systems. It aims to suppress spectral regrowth on broadband signals transmitted through a power amplifier (PA), thereby improving the overall efficiency of the PA. Generally, PAs exhibit nonlinear effects and low efficiency when processing high-power input signals. Due to spectral regrowth, nonlinear effects and spectral interference occur in adjacent frequency bands. Digital predistortion can perform linearization pre-correction processing.
[0003] However, in current digital predistortion, high sampling rates lead to hardware implementation difficulties, while low sampling rates cause aliasing problems. Summary of the Invention
[0004] This application provides a digital predistortion method and apparatus, storage medium, and computer program product. These features reduce hardware implementation complexity and provide anti-aliasing capabilities.
[0005] The technical solution of this application is implemented as follows:
[0006] In a first aspect, this application proposes a digital predistortion method, the method comprising:
[0007] The first signal is sampled using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal;
[0008] Determine the frequency domain signal corresponding to the first sampled signal, and determine the fitness value of the frequency domain signal;
[0009] If the fitness value of the frequency domain signal meets the preset conditions, digital predistortion processing is continued using the first sampled signal.
[0010] Secondly, this application proposes a digital predistortion device, the digital predistortion device comprising:
[0011] A non-uniform sampling unit is used to sample the first signal using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal.
[0012] A determining unit is configured to determine the frequency domain signal corresponding to the first sampled signal and to determine the fitness value of the frequency domain signal;
[0013] A digital predistortion unit is used to continue digital predistortion processing based on the first sampled signal when the fitness value of the frequency domain signal meets a preset condition.
[0014] Thirdly, this application proposes a digital predistortion device, which includes: a processor, a memory, and a communication bus; the communication bus is used to realize the connection and communication between the processor and the memory; the processor implements the above-mentioned digital predistortion method when executing the running program stored in the memory.
[0015] Fourthly, this application proposes a storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described digital predistortion method.
[0016] Fifthly, this application proposes a computer program product, including a computer program that, when executed by a processor, implements the above-described digital predistortion method.
[0017] This application provides a digital predistortion method and apparatus, storage medium, and computer program product. The method includes: sampling a first signal using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal; determining the frequency domain signal corresponding to the first sampled signal and determining the fitness value of the frequency domain signal; and continuing digital predistortion processing using the first sampled signal if the fitness value of the frequency domain signal meets preset conditions. By adopting the above implementation scheme, a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period are designed. The first signal is non-uniformly sampled using the non-uniform sampling period and the non-uniform sampling interval within the non-uniform sampling period to obtain the first sampled signal. The designed non-uniform sampling period and the non-uniform sampling interval within the non-uniform sampling period are based on the fitness value of the frequency domain signal corresponding to the first sampled signal obtained through non-uniform sampling. This allows the obtained non-uniform sampled signal to meet different system requirements, significantly reducing the required sampling rate without sacrificing signal quality, thereby reducing the hardware implementation difficulty. Furthermore, non-uniform sampling can also be used to achieve anti-aliasing processing in the digital predistortion process. Attached Figure Description
[0018] Figure 1 A flowchart of a digital predistortion method provided in an embodiment of this application;
[0019] Figure 2 A schematic diagram of the overall structure of an exemplary digital predistortion method provided in this application embodiment;
[0020] Figure 3 A schematic diagram of an exemplary digital predistortion device provided in this application embodiment;
[0021] Figure 4 A schematic flowchart illustrating an exemplary method for designing non-uniform sampling periods and non-uniform sampling intervals using a genetic algorithm, provided for embodiments of this application;
[0022] Figure 5 A schematic diagram of the structure of a digital predistortion device provided in this application embodiment. Figure 1 ;
[0023] Figure 6 A schematic diagram of the structure of a digital predistortion device provided in this application embodiment. Figure 2 . Detailed Implementation
[0024] In order to gain a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration only and are not intended to limit the embodiments of this application.
[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0026] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. It is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. It should also be noted that the terms "first, second, third" used in the embodiments of this application are merely for distinguishing similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.
[0027] This application provides a digital predistortion method, such as... Figure 1 As shown, the method may include:
[0028] S101. The first signal is sampled using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal.
[0029] In this embodiment, a transmitted signal is received from the transmitter; the transmitted signal is predistorted to obtain a predistorted signal; the predistorted signal is then converted from digital to analog and amplified to obtain a first signal. Subsequently, the first signal is sampled using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal.
[0030] For the overall structure of the digital predistortion method, see [link to documentation]. Figure 2 The transmitting end Tx signal (transmit signal) is input to the predistortion model, and the output predistortion signal s(n) is output. The predistortion signal s(n) is sequentially passed through a digital to analog converter (DAC) and a power amplifier (PA) to obtain a first signal u(n). Then, the first signal u(n) is sampled in a non-uniform sampling design unit to obtain a first sampled signal y(n). The predistortion model can be a commonly used memory polynomial (MP) or generalized memory polynomial (GMP) model, etc., and the specific model can be selected according to the actual situation. This application embodiment does not make specific limitations.
[0031] In this embodiment, a preset algorithm is used to design a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period. The preset algorithm includes, but is not limited to, neural networks, commonly used evolutionary algorithms (genetic algorithms, particle swarm optimization, simulated annealing, etc.), Newton's method, etc. The specific algorithm can be selected according to the actual situation, and this embodiment does not impose specific limitations.
[0032] S102. Determine the frequency domain signal corresponding to the first sampled signal, and determine the fitness value of the frequency domain signal.
[0033] In this embodiment of the application, the process of determining the frequency domain signal corresponding to the first sampled signal includes: transforming the first sampled signal based on a non-uniform sampling interval to obtain the frequency domain signal corresponding to the first sampled signal.
[0034] It should be noted that since the first sampled signal is obtained by non-uniform sampling, which is different from traditional uniform sampling, the first sampled signal after non-uniform sampling needs to be subjected to non-uniform sampling Fourier transform, as can be found in formula (1).
[0035]
[0036] Where f(n) is the frequency domain signal, y(n) is the first sampled signal, and t n Let t be the sampling time of the nth sampled signal. n+1 The sampling time of the (n+1)th sampled signal, where N is the total number of the first sampled signals.
[0037] It should be noted that, since the time intervals in traditional uniform sampling are equal, the time intervals do not affect the final result when performing a Fourier transform on a uniformly sampled signal. However, the time intervals in non-uniform sampling are all different, so the Fourier transform of non-uniform sampling needs to take into account the sampling time intervals. The first sampled signal y(n) is transformed into a frequency domain signal f(n) using the non-uniform Fourier transform.
[0038] In this embodiment of the application, the process of determining the fitness value of the frequency domain signal includes: determining the correlation data between the frequency domain signal and the second sampled signal; the second sampled signal is a uniformly sampled signal; determining the signal-to-noise ratio data of the frequency domain signal; and determining the fitness value based on the correlation data and the signal-to-noise ratio data.
[0039] It should be noted that after obtaining the frequency domain signal, the analysis of the frequency domain signal and the uniformly sampled signal h(n) is used to determine whether the designed periodic non-uniform sampling period and the non-uniform sampling interval within the non-uniform sampling period are reasonable. For this step, two criteria need to be considered: one is to perform a correlation analysis between f(n) and h(n) to obtain correlation data; the other is to analyze the signal-to-noise ratio of f(n).
[0040] Specifically, determining the correlation data between the frequency domain signal and the second sampled signal includes: performing a Fourier transform operation and conjugate processing on the second sampled signal to obtain the second signal; determining the correlation function between the frequency domain signal and the second signal; and taking the maximum value of the correlation function as the correlation data.
[0041] It should be noted that correlation analysis is performed on f(n) and h(n) to verify whether the signal after non-uniform sampling can guarantee that no signal information is lost, and to reconstruct the signal information after sampling. The correlation function between f(n) and h(n) is calculated, and the maximum value of the correlation function is taken as the correlation data, which serves as one of the criteria for judging the sampling design algorithm.
[0042] Specifically, first, perform a Fourier transform on h(n) to obtain z(n); then, take the conjugate of z(n) to obtain zi. conj (n) = Conj(z(n)), which is the second signal; finally, for f(n) and z conj (n) Convolution followed by inverse Fourier transform to calculate the correlation function.
[0043] In the embodiments of this application, the calculation process of the correlation function is shown in formula (2).
[0044] R(n) = |IFFT(f(n)*z conj (n))| (2)
[0045] Where R(n) is the correlation function.
[0046] In this embodiment, the maximum value of the correlation function is taken as the correlation data, and it is also used as the basis for judging the sampling design algorithm. The correlation data a1 is max(R(n)).
[0047] In the embodiments of this application, the process of calculating the signal-to-noise ratio data a2 of f(n) can be found in existing signal-to-noise ratio calculation methods, and will not be repeated here.
[0048] After determining the correlation data and signal-to-noise ratio data, the fitness value must be determined based on the correlation data and signal-to-noise ratio data.
[0049] It should be noted that corresponding weights are pre-assigned to the correlation data and the signal-to-noise ratio data. The aforementioned weights represent the proportions of the correlation data and the signal-to-noise ratio data in the rationality of the non-uniform sampling interval within the non-uniform sampling period of the weighted algorithm design. Then, the two data are weighted and added together to obtain the fitness value. The specific calculation process of the fitness value is shown in formula (3).
[0050] a=w1a1+w2a2 (3)
[0051] Where a is the fitness value, w1 is the weight of a1, w2 is the weight of a2, a1 is the correlation data, and a2 is the signal-to-noise ratio data.
[0052] It should be noted that w1 and w2 can be customized according to the actual capabilities of the circuit and the specific application scenario. This application embodiment does not impose specific limitations and will not be elaborated here.
[0053] S103. If the fitness value of the frequency domain signal meets the preset conditions, continue to perform digital predistortion processing based on the first sampled signal.
[0054] In this embodiment, the fitness value of the frequency domain signal is compared with a preset condition. If the fitness value of the frequency domain signal meets the preset condition, digital predistortion processing is continued using the first sampled signal.
[0055] It should be noted that the preset condition can be a judgment threshold. The judgment threshold is customized according to the actual capability of the circuit and the specific application scenario. This application embodiment does not impose specific limitations and will not be elaborated here.
[0056] In this embodiment, when the fitness value a is greater than the judgment threshold T, it indicates that the fitness value meets the preset condition; when the fitness value a is less than or equal to the judgment threshold T, it indicates that the fitness value does not meet the preset condition.
[0057] It should be noted that while the genetic algorithm toolbox in MATLAB defaults to finding the minimum value of the fitness function, this application aims to maximize the correlation coefficient and signal-to-noise ratio. Therefore, the fitness value is designed to be... At this time, when the fitness value Less than the judgment threshold When the fitness value meets the preset condition, it indicates that the fitness value is satisfied; when the fitness value is... Greater than or equal to the threshold At this time, the fitness value does not meet the preset conditions. Specific settings can be made according to actual circumstances; this application's embodiments do not impose specific limitations.
[0058] In this embodiment, when the fitness value of the frequency domain signal meets the preset conditions and / or reaches the maximum number of iterations, the iteration update of the non-uniform sampling period and non-uniform sampling interval is stopped, and the sampling signal obtained by using the designed optimal non-uniform sampling method is output to continue digital predistortion processing.
[0059] See Figure 2 The first sampled signal y(n) is processed by an analog-to-digital converter (ADC) to output signal c(n), which is then sent to the predistortion coefficient calculation module for predistortion coefficient calculation. The calculated predistortion coefficients are then transmitted to the predistortion model to obtain the predistorted first signal for the next round of predistortion processing. This process is repeated iteratively until the predistorted first signal converges, at which point the next set of transmitted signals undergoes predistortion iteration processing.
[0060] It should be noted that this application applies to the design of non-uniform sampling period and non-uniform sampling interval within non-uniform sampling period in each round of predistortion processing, under the scenario of predistortion processing for each group of transmitted signals iteratively.
[0061] The predistortion coefficient calculation architecture can be a direct learning architecture or an indirect learning architecture, and the specific choice can be made according to the actual situation. This application does not impose specific limitations on the embodiments.
[0062] Furthermore, if the fitness value of the frequency domain signal does not meet the preset conditions, the non-uniform sampling period and non-uniform sampling interval are updated, and the first signal is sampled to obtain the updated first sampled signal; the fitness value of the updated frequency domain signal corresponding to the updated first sampled signal is determined, and data predistortion processing is continued based on the updated first sampled signal until the fitness value of the updated frequency domain signal meets the preset conditions and / or the maximum number of iterations is reached.
[0063] It should be noted that updating the non-uniform sampling period and non-uniform sampling interval can be achieved through a series of methods such as selection, crossover, and mutation calculation; specifically, a random traversal selection method is used for genetic selection, and the crossover and mutation probabilities can be set according to the actual situation. This application does not impose specific limitations on the embodiments.
[0064] It is understood that the embodiments of this application leverage the advantages of non-uniform sampling to flexibly design sampling methods according to system requirements, achieving anti-aliasing while reducing the sampling rate. The embodiments of this application, through algorithmic design of the sampling period and non-uniform sampling time series, can significantly reduce the required sampling rate without sacrificing signal quality. This strategy not only reduces the need for expensive anti-aliasing filters but also lowers the overall system energy consumption and cost, bringing significant flexibility and economic benefits to hardware design. In processing high-speed signals, this proposal can utilize a low sampling rate to process high-speed signals, greatly reducing hardware design pressure and meeting the future development needs of high-speed, high-bandwidth applications.
[0065] Based on the above embodiments, this application proposes a digital predistortion device, with reference to... Figure 3 The device includes a sampling sequence design module, a non-uniform Fourier transform module, and a frequency domain correlation and signal-to-noise ratio (SNR) analysis module. The sampling sequence design module designs the non-uniform sampling period and the non-uniform sampling interval within the periodic non-uniform sampling. The non-uniform Fourier transform module performs a Fourier transform on y(n). The frequency domain correlation and SNR analysis module analyzes f(n) and h(n) to determine the rationality of the algorithm's design of the non-uniform sampling period and non-uniform sampling interval. u(n) is input into the sampling sequence design module, and y(n) is output to the non-uniform Fourier transform module to obtain f(n). Then, f(n) and h(n) are input into the frequency domain correlation and SNR analysis module. If the analysis result indicates that the fitness value of the frequency domain signal meets the preset condition (Yes), then y(n) is output to the ADC. If the analysis result indicates that the fitness value of the frequency domain signal does not meet the preset condition (No), then the process returns to the sampling sequence design module.
[0066] Based on the above embodiments, taking the design of non-uniform sampling period and non-uniform sampling interval using a genetic algorithm as an example, see [link to documentation]. Figure 4 The specific steps include:
[0067] 1. Population initialization.
[0068] It should be noted that population initialization requires encoding different sampling periods and different sampling intervals using binary encoding.
[0069] 2. Calculate the fitness value.
[0070] It should be noted that while the genetic algorithm toolbox in MATLAB defaults to finding the minimum value of the fitness function, our proposal aims for higher values for the correlation coefficient and signal-to-noise ratio. Therefore, we designed the fitness function as follows:
[0071] 3. Selection, crossover, and mutation generate new populations.
[0072] It should be noted that, if Greater than or equal to the threshold If the fitness value does not meet the preset conditions, the population is updated by selection, crossover, and mutation calculations. This involves updating different sampling periods and non-uniform sampling time series, and using a random traversal selection method for genetic selection. The crossover and mutation probabilities can be set according to the actual situation.
[0073] 4. Determine whether the maximum number of iterations has been reached, and / or whether the fitness value meets the preset conditions.
[0074] It should be noted that if the maximum number of iterations is reached and / or the fitness value meets the preset condition (Yes), the iteration stops and the optimal non-uniform sampling method is output. Non-uniform sampling is then performed according to the designed sampling sequence, and the sampled signal is input to the ADC for analog-to-digital conversion and further coefficient calculations. If the maximum number of iterations is not reached and / or the fitness value does not meet the preset condition (No), then 2 is returned.
[0075] Based on the above embodiments, this application provides a digital predistortion device. For example... Figure 5 As shown, the digital predistortion device 1 includes:
[0076] The non-uniform sampling unit 10 is used to sample the first signal using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal.
[0077] The determining unit 11 is used to determine the frequency domain signal corresponding to the first sampled signal and to determine the fitness value of the frequency domain signal;
[0078] The digital predistortion unit 12 is used to continue digital predistortion processing based on the first sampled signal when the fitness value of the frequency domain signal meets the preset conditions.
[0079] Optionally, the device further includes: a conversion unit;
[0080] The transformation unit is used to transform the first sampled signal based on the non-uniform sampling interval to obtain the frequency domain signal corresponding to the first sampled signal.
[0081] Optionally, the determining unit 11 is further configured to determine the correlation data between the frequency domain signal and the second sampled signal; the second sampled signal is a uniformly sampled signal; determine the signal-to-noise ratio data of the frequency domain signal; and determine the fitness value based on the correlation data and the signal-to-noise ratio data.
[0082] Optionally, the transformation unit is further configured to perform Fourier transform and conjugate processing on the second sampled signal to obtain the second signal;
[0083] The determining unit 11 is further configured to determine the correlation function between the frequency domain signal and the second signal; and take the maximum value of the correlation function as the correlation data.
[0084] Optionally, the device further includes: an updating unit;
[0085] The updating unit is used to update the non-uniform sampling period and the non-uniform sampling interval when the fitness value of the frequency domain signal does not meet the preset condition, and to sample the first signal to obtain the updated first sampled signal.
[0086] The determining unit 11 is further configured to determine the fitness value of the updated frequency domain signal corresponding to the updated first sampling signal, and continue to perform data predistortion processing based on the updated first sampling signal until the fitness value of the updated frequency domain signal meets the preset condition and / or reaches the maximum number of iterations.
[0087] Optionally, the device further includes: a receiving unit and a processing unit;
[0088] The receiving unit is used to receive the transmitted signal transmitted by the transmitting end;
[0089] The processing unit is configured to perform pre-distortion processing on the transmitted signal to obtain a pre-distorted signal; and to perform digital-to-analog conversion and power amplification processing on the pre-distorted signal to obtain the first signal.
[0090] This application provides a digital predistortion device that samples a first signal using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal; determines the frequency domain signal corresponding to the first sampled signal and determines the fitness value of the frequency domain signal; and continues digital predistortion processing based on the first sampled signal if the fitness value of the frequency domain signal meets preset conditions. Therefore, the digital predistortion device proposed in this embodiment designs a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period, and uses this non-uniform sampling period and non-uniform sampling interval within the non-uniform sampling period to perform non-uniform sampling of the first signal to obtain the first sampled signal. The designed non-uniform sampling period and non-uniform sampling interval within the non-uniform sampling period are based on the fitness value of the frequency domain signal corresponding to the first sampled signal obtained through non-uniform sampling. This allows the obtained non-uniform sampled signal to meet different system requirements, significantly reducing the required sampling rate without sacrificing signal quality, thereby reducing the hardware implementation difficulty. Furthermore, non-uniform sampling can be used to achieve anti-aliasing processing in the digital predistortion process.
[0091] Figure 6 A schematic diagram of the composition structure of a digital predistortion device 1 provided in this application embodiment. Figure 2 In practical applications, based on the same disclosed concept of the above embodiments, such as Figure 6 As shown, the digital predistortion device 1 in this embodiment includes: a processor 13, a memory 14, and a communication bus 15.
[0092] The processor 13 described above can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), CPU, controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic device used to implement the above processor function can also be other types, and this embodiment does not impose specific limitations.
[0093] In this embodiment, the communication bus 15 is used to establish communication between the processor 13 and the memory 14; when the processor 13 executes the running program stored in the memory 14, it implements the following digital predistortion method:
[0094] The first signal is sampled using a non-uniform sampling period and a non-uniform sampling interval within the non-uniform sampling period to obtain a first sampled signal; the frequency domain signal corresponding to the first sampled signal is determined, and the fitness value of the frequency domain signal is determined; if the fitness value of the frequency domain signal meets a preset condition, digital predistortion processing is continued based on the first sampled signal.
[0095] Furthermore, the processor 13 is also configured to transform the first sampled signal based on the non-uniform sampling interval to obtain the frequency domain signal corresponding to the first sampled signal.
[0096] Furthermore, the processor 13 is also configured to determine the correlation data between the frequency domain signal and the second sampled signal; the second sampled signal is a uniformly sampled signal; determine the signal-to-noise ratio data of the frequency domain signal; and determine the fitness value based on the correlation data and the signal-to-noise ratio data.
[0097] Furthermore, the processor 13 is also configured to perform Fourier transform and conjugate processing on the second sampled signal to obtain a second signal; determine the correlation function between the frequency domain signal and the second signal; and take the maximum value of the correlation function as the correlation data.
[0098] Furthermore, the processor 13 is also configured to update the non-uniform sampling period and the non-uniform sampling interval when the fitness value of the frequency domain signal does not meet the preset condition, and sample the first signal to obtain an updated first sampled signal; determine the fitness value of the updated frequency domain signal corresponding to the updated first sampled signal, and continue data predistortion processing based on the updated first sampled signal until the fitness value of the updated frequency domain signal meets the preset condition and / or reaches the maximum number of iterations.
[0099] Furthermore, the processor 13 is also used to receive the transmitted signal from the transmitter; and to perform predistortion processing on the transmitted signal to obtain a predistorted signal;
[0100] The predistorted signal is subjected to digital-to-analog conversion and power amplification to obtain the first signal.
[0101] This application provides a storage medium storing a computer program thereon. The computer-readable storage medium stores one or more programs, which can be executed by one or more processors and applied in a digital predistortion device. The computer program implements the digital predistortion method as described above.
[0102] Based on the above embodiments, this application provides a computer program product, including a computer program that can be executed by one or more processors, and the computer program implements the digital predistortion method as described above.
[0103] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause an image display device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.
[0105] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application.
Claims
1. A method of digital pre-distortion, characterized by, The method comprises: sampling a first signal by using a non-uniform sampling period and a non-uniform sampling interval in the non-uniform sampling period to obtain a first sampling signal; determining a frequency domain signal corresponding to the first sampling signal and determining an adaptability value of the frequency domain signal; in a case where the adaptability value of the frequency domain signal meets a preset condition, continuing digital pre-distortion processing by using the first sampling signal.
2. The method of claim 1, wherein, The determination of the frequency domain signal corresponding to the first sampling signal comprises: transforming the first sampling signal based on the non-uniform sampling interval to obtain the frequency domain signal corresponding to the first sampling signal.
3. The method of claim 1, wherein, The determination of the adaptability value of the frequency domain signal comprises: determining correlation data between the frequency domain signal and a second sampling signal; the second sampling signal is a uniform sampling signal; determining signal-to-noise ratio data of the frequency domain signal; determining the adaptability value based on the correlation data and the signal-to-noise ratio data.
4. The method of claim 3, wherein, The determination of the correlation data between the frequency domain signal and the second sampling signal comprises: performing Fourier transform operation and conjugate processing on the second sampling signal to obtain a second signal; determining a correlation function between the frequency domain signal and the second signal; taking a maximum value of the correlation function as the correlation data.
5. The method of claim 1, wherein, After the determination of the frequency domain signal corresponding to the first sampling signal and the determination of the adaptability value of the frequency domain signal, the method further comprises: in a case where the adaptability value of the frequency domain signal does not meet the preset condition, updating the non-uniform sampling period and the non-uniform sampling interval and sampling the first signal to obtain an updated first sampling signal; determining an adaptability value of an updated frequency domain signal corresponding to the updated first sampling signal until the adaptability value of the updated frequency domain signal meets the preset condition and / or a maximum iteration number is reached, and continuing digital pre-distortion processing by using the updated first sampling signal.
6. The method of claim 1, wherein, Before the sampling of the first signal by using the non-uniform sampling period and the non-uniform sampling interval in the non-uniform sampling period to obtain the first sampling signal, the method further comprises: receiving a sending signal transmitted by a transmitting end; performing pre-distortion processing on the sending signal to obtain a pre-distorted signal; performing digital-to-analog conversion processing and power amplification processing on the pre-distorted signal to obtain the first signal.
7. A digital pre-distortion apparatus characterized by comprising: The digital pre-distortion device comprises: a non-uniform sampling unit configured to sample a first signal by using a non-uniform sampling period and a non-uniform sampling interval in the non-uniform sampling period to obtain a first sampling signal; a determination unit configured to determine a frequency domain signal corresponding to the first sampling signal and determine an adaptability value of the frequency domain signal; a digital pre-distortion unit configured to, in a case where the adaptability value of the frequency domain signal meets a preset condition, continue digital pre-distortion processing based on the first sampling signal.
8. A digital pre-distortion apparatus characterized by comprising: The digital pre-distortion device comprises a processor, a memory and a communication bus; the communication bus is used to realize the connection communication between the processor and the memory; the processor realizes the method as claimed in any one of claims 1-6 when executing the running program stored in the memory.
9. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the method as claimed in any one of claims 1-6.
10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the method as claimed in any one of claims 1-6.