Pre-distortion model selection method and device and device for pre-distortion model selection

Abstract

The invention provides a predistortion model selection method, a predistortion model selection device and a device for predistortion model selection. The method comprises the following steps: creating an initial population according to initial model configuration parameters of a pre-distortion model; performing n iterations based on the initial population, and outputting a target population after the nth iteration; in the mth iteration process, executing the following steps: updating the population after the (m-1) th iteration by adopting a genetic algorithm to obtain the population after the mth iteration; according to the fitness of each individual in the population after the (m-1) th iteration, selecting a specified number of historical optimal individuals from the population obtained by the (m-1) th iteration; updating the target population after (m-1) th iteration according to each individual in the population after (m-1) th iteration and a specified number of historical optimal individuals; and obtaining target model configuration parameters of the pre-distortion model according to the target population after the nth iteration.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a pre-distortion model selection method, device and a device for pre-distortion model selection. Background Art

[0002] Digital Predistortion (DPD) technology is an important technical means for correcting the linearity of power amplifiers. The key to DPD technology is to obtain accurate model parameters of the predistortion model, which requires configuration and optimization for a large number of model parameter values, resulting in high hardware resources occupied by the predistortion model selection. Summary of the invention

[0003] It would be advantageous to provide a mechanism that mitigates, alleviates or eliminates at least one of the problems discussed above.

[0004] In a first aspect, a pre-distortion model selection method is provided, comprising: Creating an initial population according to initial model configuration parameters of a predistortion model, wherein the initial model configuration parameters are obtained according to a power amplifier output signal of a power amplifier corresponding to the predistortion model, and the power amplifier input signal of the power amplifier is obtained by predistorting a forward DPD input signal according to the predistortion model; Perform n iterations based on the initial population, and output the target population after the nth iteration; wherein 1≤m≤n, m and n are integers, and in the mth iteration process, perform the following steps: Use the genetic algorithm to update the population after the m-1th iteration to obtain the population after the mth iteration; Calculating the fitness of each individual in the population after the m-1th iteration, and then selecting a specified number of historical optimal individuals from the population obtained by the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; According to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals, the target population after the m-1th iteration is updated to obtain the target population after the mth iteration; The target model configuration parameters of the predistortion model are obtained according to the target population after the nth iteration.

[0005] In a second aspect, a pre-distortion model selection device is provided, comprising: an initial population creation module, a population iteration module, and a configuration parameter output module; The initial population creation module is used to create an initial population according to the initial model configuration parameters of the predistortion model, wherein the initial model configuration parameters are obtained according to the power amplifier output signal of the power amplifier corresponding to the predistortion model, and the initial population is obtained by encoding according to the search parameters of the predistortion model; The population iteration module is used to update the population after the m-1th iteration by using a genetic algorithm to obtain the population after the m-1th iteration; calculate the fitness of each individual in the population after the m-1th iteration, and then select a specified number of historical optimal individuals from the population obtained by the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; update the target population after the m-1th iteration according to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals to obtain the target population after the m-1th iteration; The configuration parameter output module is used to obtain the target model configuration parameters of the pre-distortion model according to the target population after the nth iteration.

[0006] In a third aspect, a device for selecting a predistortion model is provided. The device comprises: a predistortion DPD module, a power amplifier conversion module, a DPD modeling module, and an error ERR module; wherein: The DPD module is used to perform pre-distortion processing on the forward DPD input signal and output the power amplifier input signal; The power amplifier conversion module converts the power amplifier input signal into a power amplifier output signal; The ERR module is used to calculate the error of the predistortion model according to the power amplifier input signal and the power amplifier output signal output by the DPD modeling module; The DPD modeling module is used to implement the pre-distortion model selection method as described in the first aspect according to the error and the power amplifier output signal, wherein the target model configuration parameters obtained by the DPD modeling module are copied to the DPD module.

[0007] In a fourth aspect, a power amplifier is provided, comprising: a device for the method described in the first aspect, or a pre-distortion model selection device as described in the second aspect, or a device for pre-distortion model selection as described in the third aspect.

[0008] In a fifth aspect, a base station is provided, comprising the power amplifier as described in the fourth aspect.

[0009] In a sixth aspect, a base station is provided. The base station includes: one or more processors; and one or more memories coupled to the one or more processors and storing instructions thereon. When the instructions are executed by the one or more processors individually or collectively, the base station executes any one of the methods in the first aspect above.

[0010] In a seventh aspect, a non-transitory computer-readable storage medium storing machine-executable instructions is provided. The machine-executable instructions, when executed individually or collectively by one or more processors of a machine, cause the machine to perform any one of the above methods.

[0011] In an eighth aspect, a computer program product is provided comprising machine executable instructions. The machine executable instructions, when executed individually or collectively by one or more processors of a machine, cause the machine to perform any one of the above methods.

[0012] In a ninth aspect, a circuit system is provided, wherein the circuit system is configured to execute the method according to any one of the first aspect.

[0013] It should be understood that the invention summary is not intended to identify the key or essential features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of some embodiments of the present disclosure in the accompanying drawings, in which: Figure 1 A flow chart of a pre-distortion model selection method according to some embodiments of the present disclosure is shown; Figure 2 is an example diagram of a process for updating a target population according to some embodiments of the present disclosure; Figure 3 is a logic diagram of a pre-distortion model selection method according to some embodiments of the present disclosure; Figure 4 A schematic diagram of a pre-distortion model selection device according to some embodiments of the present disclosure is shown; Figure 5 A schematic diagram of a device for pre-distortion model selection according to some embodiments of the present disclosure is shown; Figure 6 A schematic diagram of a DPD modeling module according to some embodiments of the present disclosure is shown; Figure 7 Schematic diagram of a DPD modeling module according to some other embodiments of the present disclosure; Figure 8 A simplified block diagram of a base station suitable for implementing the exemplary embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] The principle of the present disclosure will now be described with reference to some embodiments. It should be understood that the description of these embodiments is only for illustrative purposes, and helps those skilled in the art to understand and implement the present disclosure, without any limitation to the scope of the present disclosure. The disclosure described herein can be implemented in a manner different from that described below.

[0016] In the following description and claims, unless defined otherwise, 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 disclosure belongs.

[0017] References in this disclosure to "one embodiment," "an embodiment," "an exemplary embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in conjunction with an exemplary embodiment, whether or not explicitly described, those skilled in the art will recognize that such feature, structure, or characteristic affects incorporation into other embodiments.

[0018] It should be understood that although the terms "first" and "second" etc. may be used to describe various elements herein, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the listed terms.

[0019] The terms used herein are only for describing specific embodiments, rather than for limiting exemplary embodiments. The singular forms "one", "an", and "the" used herein also include plural forms, unless the context clearly indicates otherwise. "A group of elements" or "element set" used herein is intended to include one or more elements. It should also be understood that the terms "include", "comprise", "have", "have", "include" and / or "include", when used herein, specify the presence of the features, elements and / or parts, etc., but do not exclude the presence or addition of one or more other features, elements, parts and / or combinations thereof.

[0020] As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) Hardware circuit implementation only (e.g., implementation only in analog and / or digital circuits) (b) a combination of hardware circuitry and software, such as (where applicable): (i) a combination of analog and / or digital hardware circuitry and software / firmware; and (ii) any part of a hardware processor (including a digital signal processor) with software, software and memory that work together to enable a device such as a mobile phone or server to perform various functions, and (c) A hardware circuit and / or processor, such as a microprocessor or portion of a microprocessor, that requires software (eg, firmware) to operate, but where software is not required for operation, the software may not be present.

[0021] This definition of circuitry applies to all uses of this term in this application, including in any claims. As another example, as used in this application, the term circuitry also includes an implementation of merely a hardware circuit or processor (or multiple processors) or a portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also includes, for example, if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device, or a similar integrated circuit in a server, cellular network device, or other computing network device.

[0022] The term "communication device" refers to a device capable of wireless communication. As an example and not a limitation, a communication device may refer to a base station, a terminal device, a receiver, a transmitter, and the like. These communication devices may follow any appropriate communication standard network, such as long-term evolution (LTE), LTE-Advanced (LTE-A), wideband code division multiple access (WCDMA), high-speed packet access (HSPA), narrowband Internet of Things (NB-IOT), new radio (NR), non-terrestrial network (NTN), etc. In addition, the communication between different communication devices may be performed according to any appropriate generation of communication protocols, including but not limited to first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), future sixth generation (6G) communication protocols, and / or any other protocols currently known or to be developed in the future. The embodiments of the present disclosure may be applied to solve the DPD model selection problem of the power amplifier in these communication devices. In the following description, the term "communication device" may be understood as any of the above devices.

[0023] In the existing solutions for pre-distortion model selection, the method usually adopted is to provide the search space required by the genetic algorithm under the premise of ensuring the fitting accuracy of the pre-distortion model to the power amplifier characteristics, and then configure and optimize the values ​​of the massive model parameters in the search space. Due to the large search space, the convergence speed of the genetic algorithm is slow, and the required calculation amount is large, resulting in a high hardware resource required for implementing the pre-distortion model selection.

[0024] The embodiment of the present disclosure proposes a pre-distortion model selection scheme to improve the convergence speed of the genetic algorithm in the DPD technology and save the hardware resources occupied by the model selection. In this solution, the communication device (such as a base station, etc.) updates the iterative target population by using the historical results retained when the genetic algorithm updates the iterative population, and determines the target model configuration parameters of the pre-distortion model through the target population, which can achieve the effect of selecting the best from the best. Therefore, according to the exemplary embodiment, the convergence speed of the genetic algorithm is improved, the amount of calculation required for the algorithm convergence is reduced, and the hardware resources occupied by the pre-distortion model selection are reduced.

[0025] The following will refer to Figure 1 The principles and implementations of the present disclosure are described in detail.

[0026] Figure 1 A flowchart showing an exemplary process 100 of a predistortion model selection method according to some embodiments of the present disclosure includes: S101, creating an initial population according to initial model configuration parameters of a pre-distortion model.

[0027] The initial model configuration parameters are obtained according to the power amplifier output signal of the power amplifier corresponding to the predistortion model, and the power amplifier input signal of the power amplifier is obtained by predistorting the forward DPD input signal according to the predistortion model.

[0028] In some embodiments, the predistortion model uses a linear spline segmentation model or a generalized polynomial model. The generalized polynomial model (also called the GMP model) is a model that is currently widely used in communication equipment such as base stations. Its advantages are high modeling accuracy and easy hardware implementation.

[0029] The expression of the generalized polynomial model is shown in the following formula (1): k (1) in, For the amplifier output signal, is the input signal of the power amplifier, m is the memory depth value of the signal term of the pre-distortion model, l is the value of the modulus term of the pre-distortion model, is the nonlinear order, K is the maximum value of the nonlinear order, M is the maximum value of the memory depth of the signal term, and L is the maximum value of the memory depth of the modulus term. It is the coefficient of the term when the parameter value combination is (m, l, k).

[0030] refer to Figure 5 and the expression (1) of the generalized polynomial model, it can be seen that the search parameters of the generalized polynomial model include: M1-M U 、L1-L Uand K1-K U , where U is the number of parameter value combinations (m, l, k) in the search space.

[0031] The expression of the first-order spline segmentation model is shown in the following formula (2): (2) in, For the amplifier output signal, is the input signal of the power amplifier, m is the memory depth value of the signal term of the pre-distortion model, l is the value of the modulus term of the pre-distortion model, is a spline function, which represents the kth segment function of the model term at the corresponding memory matrix position (m, l), K is the maximum spline division value, M is the maximum value of the memory depth of the signal term, and L is the maximum value of the memory depth of the modulus term.

[0032] refer to Figure 6 and one From the expression of the sub-spline segmentation model, it can be seen that the search parameters of the first-order spline segmentation model include: M1-M U 、L1-L U , where U is the number of parameter value combinations (m, l) in the search space.

[0033] It can be seen that compared with the generalized polynomial model, the linear spline segmented model does not need to calculate the high-order nonlinear modulus terms in the traditional memory polynomial, so it can fit any order of nonlinearity with smaller computing resources, and eliminate the influence of the order of the nonlinear model to reduce the optimal solution space, thereby reducing the amount of calculation required by the genetic algorithm and helping to save the hardware resources occupied by the pre-distortion model selection.

[0034] The following is a subsequent explanation using the first-order spline segmented model as an example.

[0035] Specifically, for a linear spline segmented model, an initial population is created according to the initial model configuration parameters of the pre-distortion model, including: obtaining U parameter value combinations according to the search parameters of the pre-distortion model, encoding the U parameter value combinations to obtain corresponding individuals, wherein the parameter value combination is composed of the value of the signal memory item and the value of the modulus memory item, and U is an integer greater than one; encoding N individuals to obtain an initial population, wherein N is an integer greater than one.

[0036] For example, the U group (m u , l u ) form a vector ML, let ML=[m0, m1, … m U , l0, l1,… lU ] Define ML as an "individual", then repeat to obtain N MLs, and construct the matrix [ML1; ML2; ...ML N ] is defined as the “initial population”.

[0037] S102, performing n iterations based on the initial population, and outputting a target population after the nth iteration.

[0038] Where 1≤m≤n, m and n are integers. During the mth iteration, the following steps are performed: A genetic algorithm is used to update the population after the m-1th iteration to obtain the population after the mth iteration.

[0039] Calculate the fitness of each individual in the population after the m-1th iteration, and then select a specified number of historical optimal individuals from the population obtained in the m-1th iteration based on the fitness of each individual in the population after the m-1th iteration.

[0040] The target population after the m-1th iteration is updated according to each individual in the population after the m-1th iteration and a specified number of historical optimal individuals to obtain the target population after the mth iteration.

[0041] In some embodiments, a genetic algorithm is used to update the population after the m-1th iteration to obtain the population after the m-1th iteration, including: using a preset fitness function to calculate the fitness of each individual in the population after the m-1th iteration; performing selection operations, crossover operations and mutation operations on the population after the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration to obtain the population after the m-1th iteration.

[0042] The expression of the fitness function is shown in the following formula (3): (3) in, Modeling error when the target model configuration parameters of the predistortion model are ML.

[0043] make When minimizing, calculate the fitness of each individual in the population. Alternatively, you can also define the inverse function of the fitness function , then calculate the fitness of each individual in the population when the reciprocal function is maximized. The expression of is shown in formula (4): (4) The calculation formula is shown in formula (5): (5) Among them, x is the coefficient vector to be solved, A is the reference signal matrix composed of power amplifier output data and spline function, and b is the error vector composed of initial error.

[0044] Next, the steps of selecting the operation include: 1. According to the fitness of each individual in the population, calculate the normalized fitness probability sum value of the population.

[0045] The calculation formula of the normalized fitness probability sum value is shown in formula (6): (6) Among them, i and j are the row and column numbers of the individual in the population, n is 1-N, and N is an integer greater than one.

[0046] 2. Randomly generate a sequence of increasing probability thresholds of length N.

[0047] 3. According to the normalized fitness probability sum value and the increasing probability threshold sequence, a specified number of individuals are selected and included in the new population.

[0048] 4. Replace the population with a new population.

[0049] The value of the increasing probability threshold sequence is from 0 to 1.

[0050] Next, the crossover operation steps include: 1. Set the crossover probability.

[0051] 2. Randomly select two adjacent individuals in the population.

[0052] 3. Use the comparison result of random numbers and crossover probability to cross-exchange the binary sequences of two adjacent individuals until all individuals in the population have been selected.

[0053] The crossover probability is between 0 and 1. The random number is between 0 and 1. If the random number is greater than the crossover probability, the two adjacent individuals currently selected will not be processed, and the next group of individuals will be selected. If the random number is less than or equal to the crossover probability, the binary sequence of the two adjacent individuals (that is, the elements in the individual) will be replaced by the crossover probability. The sequences corresponding to the binary encoding are cross-exchanged until all individuals have been selected.

[0054] Next, the mutation operation steps include: 1. Set the mutation probability.

[0055] 2. Select a random bit from the binary sequence of each individual in the population.

[0056] 3. Use mutation probability to flip random bits.

[0057] The mutation probability ranges from 0 to 1. Furthermore, after performing selection, crossover and mutation operations on the population according to the fitness of each individual in the population, a specified number of historical optimal individuals are selected from the population obtained in the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; the target population after the m-1th iteration is updated according to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals to obtain the target population after the m-1th iteration.

[0058] It will be understood that when m is 1, the population after the m-1th iteration refers to the initial population.

[0059] like Figure 2 As shown, the first three iterations are used as examples for explanation. Before iteration, a specified number of historically optimal individuals are selected from the initial population as the initial target population, and then in the first iteration, a new target population is obtained based on the initial target population and the population obtained in the first iteration. Similarly, in the second iteration, based on the fitness of each individual in the population after the first iteration, a specified number of historically optimal individuals are selected from the population obtained in the first iteration, and then the target population after the first iteration is updated based on each individual in the population after the first iteration and these new specified number of historically optimal individuals, so that the target population after the second iteration can be obtained. Similarly, in the third iteration, based on the fitness of each individual in the population after the second iteration, a specified number of historically optimal individuals are selected from the population obtained in the second iteration, and then the target population after the second iteration is updated based on each individual in the population after the second iteration and these new specified number of historically optimal individuals, so that the target population after the third iteration can be obtained.

[0060] Among them, the specified number of historical optimal individuals can be obtained by sorting the fitness of all individuals in the current population and the historical population in the previous iteration process.

[0061] For example, the fitness is sorted from large to small, and then a specified number of individuals are selected. Since the current population and the target population are updated successively during the update iteration process, the best of the best can be selected, which helps to improve the convergence speed of the genetic algorithm and reduce the amount of calculation required for the algorithm to converge.

[0062] S103, obtaining target model configuration parameters of the predistortion model according to the target population after the nth iteration.

[0063] In some embodiments, the target model configuration parameters of the pre-distortion model are obtained according to the target population after the nth iteration, including: using a preset fitness function to calculate the fitness of each individual in the target population after the nth iteration; sorting the obtained fitnesses and selecting the maximum fitness among the fitnesses; when the maximum fitness exceeds a preset fitness threshold, taking the individual corresponding to the maximum fitness as the target individual; and taking the configuration parameters corresponding to the target individual as the target model configuration parameters of the pre-distortion model.

[0064] Figure 3 is a logic diagram of a pre-distortion model selection method according to some embodiments of the present disclosure. Figure 3 In the embodiment of the present disclosure, when selecting a pre-distortion model, it is first necessary to create an initial population and calculate the fitness, then perform selection operations, crossover operations, and mutation operations in sequence, and then perform fitness calculations and NBEST population updates again for the new population formed after the operations. The inner loop in the logic of this solution is formed by two judgment conditions: whether the pre-distortion model meets the termination condition and whether the number of population iterations meets the specified number of times. When the number of population iterations meets the specified number of times, the operation of replacing the initial population with the NBEST population is triggered, and the fitness calculation is returned to form the outer loop of the logic of this solution. It can be seen that in the embodiment of the present disclosure, the inner loop and the outer loop together play the role of selecting the best from the best. Therefore, according to the exemplary embodiment, the convergence speed of the genetic algorithm is improved, the amount of calculation required for the convergence of the algorithm is reduced, and the hardware resources required to realize the selection of the pre-distortion model are reduced.

[0065] See also Figure 4 An embodiment of a pre-distortion model selection device 200 provided in an embodiment of the present disclosure includes: an initial population creation module 201, a population iteration module 202 and a configuration parameter output module 203; wherein: An initial population creation module 201 is used to create an initial population according to the initial model configuration parameters of the pre-distortion model; The population iteration module 202 is used to update the population after the m-1th iteration by using a genetic algorithm to obtain the population after the m-1th iteration; calculate the fitness of each individual in the population after the m-1th iteration, and then select a specified number of historical optimal individuals from the population obtained by the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; update the target population after the m-1th iteration according to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals to obtain the target population after the m-1th iteration; The configuration parameter output module 203 is used to obtain the target model configuration parameters of the predistortion model according to the target population after the nth iteration.

[0066] In some embodiments, the initial population creation module 201 is used to: Obtaining U parameter value combinations according to the search parameters of the predistortion model, encoding the U parameter value combinations to obtain corresponding individuals, wherein the parameter value combination is composed of the value of the signal memory item and the value of the modulus memory item, and U is an integer greater than one; Encode N individuals to obtain an initial population, where N is an integer greater than one.

[0067] In some embodiments, the population iteration module 202 is used to: The preset fitness function is used to calculate the fitness of each individual in the target population after the nth iteration; Sort the obtained fitnesses and select the maximum fitness among them; When the maximum fitness exceeds the preset fitness threshold, the individual corresponding to the maximum fitness is taken as the target individual; and, The configuration parameters corresponding to the target individual are used as the target model configuration parameters of the predistortion model.

[0068] In some embodiments, the population iteration module 202 is used to: The preset fitness function is used to calculate the fitness of each individual in the population after the m-1th iteration; According to the fitness of each individual in the population after the m-1th iteration, selection operation, crossover operation and mutation operation are performed on the population after the m-1th iteration to obtain the population after the mth iteration.

[0069] In some embodiments, the population iteration module 202 is used to: According to the fitness of each individual in the population, calculate the normalized fitness probability sum value of the population; An increasing probability threshold sequence of length N is randomly generated, wherein the value of the increasing probability threshold sequence is from 0 to 1, and N is an integer greater than one.

[0070] Selecting a specified number of individuals to be included in a new population according to the normalized fitness probability sum value and the increasing probability threshold sequence; and, Replace the population with a new population.

[0071] In some embodiments, the population iteration module 202 is used to: Set the crossover probability, where the crossover probability value ranges from 0 to 1; Randomly select two adjacent individuals in the population; The binary sequences of two adjacent individuals are cross-exchanged using the comparison result of the random number and the crossover probability until all individuals in the population have been selected.

[0072] In some embodiments, the population iteration module 202 is used to: Set the mutation probability, where the mutation probability value ranges from 0 to 1; Selecting a random bit from the binary sequence for each individual in the population; and, Using mutation probability, flip random bits.

[0073] Furthermore, Figure 5 3 is a schematic diagram of a device 300 for predistortion model selection provided by an embodiment of the present disclosure. The device 300 includes: a predistortion DPD module 301, a power amplifier conversion module 302, a DPD modeling module 303, and an error ERR module 304; The DPD module 301 is used to perform pre-distortion processing on the forward DPD input signal and output the power amplifier input signal; The power amplifier conversion module 302 converts the power amplifier input signal into a power amplifier output signal; The ERR module 303 is used to calculate the error of the predistortion model according to the power amplifier input signal and the power amplifier output signal output by the DPD modeling module 304; The DPD modeling module 304 is used to implement the above-mentioned pre-distortion model selection method according to the error and the power amplifier output signal, wherein the target model configuration parameters obtained by the DPD modeling module 304 are copied to the DPD module.

[0074] The power amplifier conversion module 302 may specifically include: a digital-to-analog conversion DAC unit 3021, a radio frequency PA unit 3022, and an analog-to-digital conversion ADC unit 3033. The DAC unit 3021 is used to perform digital-to-analog conversion on the power amplifier input signal and send it to the PA unit 3022. The PA unit 3022 is used to modulate the power amplifier input signal after digital-to-analog conversion and send it to the ADC unit 3023. The ADC unit 3023 is used to perform analog-to-digital conversion on the power amplifier input signal after digital-to-analog conversion and modulation to obtain a power amplifier output signal.

[0075] Optionally, the pre-distortion model in the DPD modeling module 304 is a generalized polynomial model.

[0076] refer to Figure 6 , the DPD modeling module 304 includes: a coefficient solving module 3041, U first search units 3042, U second search units 3043 and U third search units 3044, where U is an integer greater than 1; The coefficient solving module 3041 is used to update the search parameters of the predistortion model according to the power amplifier input signal and the power amplifier output signal; The first search unit 3042 is used to obtain the memory depth value of the signal item in the parameter value combination according to the search parameter; The second search unit 3043 is used to obtain the memory depth value of the module value item in the parameter value combination according to the search parameter; The third search unit 3044 is used to obtain the nonlinear order in the parameter value combination according to the search parameter.

[0077] Optionally, the pre-distortion model in the DPD modeling module 304 is a linear spline segmentation model.

[0078] refer to Figure 7 , the DPD modeling module 304 includes: a coefficient solving module 3041, U first search units 3042, and U second search units 3043, where U is an integer greater than 1; The coefficient solving module 3041 is used to update the search parameters of the predistortion model according to the power amplifier input signal and the power amplifier output signal; The first search unit 3042 is used to obtain the memory depth value of the signal item in the parameter value combination according to the search parameter; The second search unit 3043 is used to obtain the memory depth value of the module value item in the parameter value combination according to the search parameter.

[0079] Furthermore, an embodiment of the present disclosure provides a power amplifier, which includes a device for executing the predistortion model selection method 100, or a predistortion model selection device 200, or a device for predistortion model selection 300 as described above.

[0080] Furthermore, an embodiment of the present disclosure provides a base station, which includes the power amplifier mentioned above.

[0081] Furthermore, Figure 8 4 is a schematic diagram of a base station 400 provided by an embodiment of the present disclosure. As shown in the figure, the base station 400 includes one or more processors 410, one or more memories 420 coupled to the processors 410, and one or more communication modules 440 coupled to the processors 410.

[0082] The communication module 440 is used for two-way communication. The communication module 440 has at least one antenna to facilitate communication. The communication interface may represent any interface necessary for communicating with other network elements.

[0083] Processor 410 may be of any type suitable for the local technology network, and may include, as non-limiting examples, one or more of: a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Base station 400 may have multiple processors, such as application specific integrated circuit chips, which are driven in time to a clock that synchronizes a master processor.

[0084] The memory 420 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 424, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disk (CD), digital video disk (DVD), and other magnetic and / or optical memories. Examples of volatile memories include, but are not limited to, random access memory (RAM) 422 and other volatile memories that do not persist during the duration of a power outage.

[0085] Computer program 430 includes computer executable instructions that are executed by associated processor 410. Program 430 may be stored in ROM 424. Processor 410 may perform any appropriate actions and processes by loading program 430 into RAM 422.

[0086] The embodiments of the present disclosure may be implemented by a program 430, so that the base station 400 may execute the reference Figure 1 Any process of the disclosure discussed. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0087] In some embodiments, the program 430 may be tangibly contained in a computer-readable medium, which may be contained in other storage devices accessible to the base station 400. The base station 400 may load the program 430 from the computer-readable medium to the RAM 422 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. The computer-readable medium has the program 430 stored thereon.

[0088] Generally, various embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0089] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the above-mentioned reference Figure 1The method 100 is described. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or separated between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0090] The program code for executing the disclosed method can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing equipment so that when the program code is executed by the processor or controller, the function / operation specified in the flow chart and / or block diagram is realized. The program code can be executed completely on the machine as an independent software package, partially on the machine, partially on the machine, partially on a remote machine, partially on a remote machine, or all on a remote machine or server.

[0091] In the context of the present disclosure, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer readable media, etc.

[0092] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or apparatuses, or any suitable combination of the foregoing. More specific examples of computer readable storage media include an electrical connection having one or more conductors, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] The present disclosure also provides a chip system, including a circuit system, wherein the circuit system is configured to execute the pre-distortion model selection method as described above.

[0094] Although operations are described in a specific order, this should not be understood as requiring to perform these operations in the specific order or sequence shown, or to perform all the operations shown, to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be interpreted as limitations on the scope of the present disclosure, but can be interpreted as descriptions of features specific to a particular embodiment. Certain features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0095] Although the disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0096] It should be fully understood that the use of personally identifiable information should be subject to privacy policies and practices generally recognized as meeting or exceeding industry or government requirements for maintaining user privacy. In particular, personally identifiable information data should be managed and processed to minimize the risk of inadvertent or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

Claims

1. A method for selecting a predistortion model, characterized in that: include: Creating an initial population according to initial model configuration parameters of a predistortion model, wherein the initial model configuration parameters are obtained according to a power amplifier output signal of a power amplifier corresponding to the predistortion model, and the power amplifier input signal of the power amplifier is obtained by predistorting a forward DPD input signal according to the predistortion model; Perform n iterations based on the initial population, and output the target population after the nth iteration; wherein 1≤m≤n, m and n are integers, and in the mth iteration process, perform the following steps: Use the genetic algorithm to update the population after the m-1th iteration to obtain the population after the mth iteration; Calculating the fitness of each individual in the population after the m-1th iteration, and then selecting a specified number of historical optimal individuals from the population obtained by the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; According to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals, the target population after the m-1th iteration is updated to obtain the target population after the mth iteration; The target model configuration parameters of the predistortion model are obtained according to the target population after the nth iteration.

2. The method according to claim 1, characterized in that The predistortion model is a linear spline segmentation model or a generalized polynomial model.

3. The method according to claim 1, characterized in that The step of creating an initial population according to the initial model configuration parameters of the predistortion model includes: Obtaining U parameter value combinations according to the search parameters of the predistortion model, encoding the U parameter value combinations to obtain corresponding individuals, wherein the parameter value combination is composed of the value of the signal memory item and the value of the modulus memory item, and U is an integer greater than one; Encode N of the individuals to obtain the initial population, where N is an integer greater than one.

4. The method according to claim 1, characterized in that The step of obtaining the target model configuration parameters of the predistortion model according to the target population after the nth iteration includes: Using a preset fitness function to calculate the fitness of each individual in the target population after the nth iteration; Sorting the obtained fitnesses and selecting the maximum fitness among the fitnesses; When the maximum fitness exceeds a preset fitness threshold, taking the individual corresponding to the maximum fitness as the target individual; and The configuration parameters corresponding to the target individual are used as the target model configuration parameters of the predistortion model.

5. The method according to any one of claims 1 to 4, characterized in that: The method of using a genetic algorithm to update the population after the m-1th iteration to obtain the population after the mth iteration includes: Using a preset fitness function to calculate the fitness of each individual in the population after the m-1th iteration; According to the fitness of each individual in the population after the m-1th iteration, selection operation, crossover operation and mutation operation are performed on the population after the m-1th iteration to obtain the population after the mth iteration.

6. The method according to claim 5, characterized in that The expression of the fitness function is: ,in The modeling error when the target model configuration parameter of the pre-distortion model is ML.

7. The method according to claim 5, characterized in that The steps of the selection operation include: Calculating a normalized fitness probability sum value of the population according to the fitness of each individual in the population; Randomly generate an increasing probability threshold sequence of length N, wherein the value of the increasing probability threshold sequence is from 0 to 1, and N is an integer greater than one; Selecting a specified number of individuals according to the normalized fitness probability sum value and the increasing probability threshold sequence to be included in a new population; and, The population is replaced by the new population.

8. The method according to claim 5, characterized in that The crossover operation steps include: Setting a crossover probability, wherein the crossover probability ranges from 0 to 1; Randomly select two adjacent individuals from the population; The binary sequences of the two adjacent individuals are cross-exchanged using the comparison result of the random number and the crossover probability until all individuals in the population have been selected.

9. The method according to claim 5, characterized in that The mutation operation step includes: Setting a mutation probability, wherein the mutation probability ranges from 0 to 1; selecting a random bit from the binary sequence of each individual in the population; and, The random bits are flipped using the mutation probability.

10. A predistortion model selection device, characterized in that: include: Initial population creation module, population iteration module and configuration parameter output module; The initial population creation module is used to create an initial population according to the initial model configuration parameters of the predistortion model, wherein the initial model configuration parameters are obtained according to the power amplifier output signal of the power amplifier corresponding to the predistortion model, and the power amplifier input signal of the power amplifier is obtained by predistorting the forward DPD input signal according to the predistortion model; The population iteration module is used to update the population after the m-1th iteration by using a genetic algorithm to obtain the population after the m-1th iteration; calculate the fitness of each individual in the population after the m-1th iteration, and then select a specified number of historical optimal individuals from the population obtained by the m-1th iteration according to the fitness of each individual in the population after the m-1th iteration; update the target population after the m-1th iteration according to each individual in the population after the m-1th iteration and the specified number of historical optimal individuals to obtain the target population after the m-1th iteration; The configuration parameter output module is used to obtain the target model configuration parameters of the pre-distortion model according to the target population after the nth iteration.

11. A device for selecting a predistortion model, characterized in that: include: Predistortion DPD module, power amplifier conversion module, DPD modeling module, error ERR module; among which: The DPD module is used to perform pre-distortion processing on the forward DPD input signal and output the power amplifier input signal; The power amplifier conversion module converts the power amplifier input signal into a power amplifier output signal; The ERR module is used to calculate the error of the predistortion model according to the power amplifier input signal and the power amplifier output signal output by the DPD modeling module; The DPD modeling module is used to implement the pre-distortion model selection method as described in any one of claims 1-9 according to the error and the power amplifier output signal, wherein the target model configuration parameters obtained by the DPD modeling module are copied to the DPD module.

12. The device according to claim 11, characterized in that The DPD modeling module includes: a coefficient solving module, U first search units, U second search units and U third search units, where U is an integer greater than 1; The coefficient solving module is used for updating the search parameters of the predistortion model according to the power amplifier input signal and the power amplifier output signal; The first search unit is used to obtain a memory depth value of a signal item in a parameter value combination according to the search parameter; The second search unit is used to obtain a memory depth value of a module value item in the parameter value combination according to the search parameter; The third search unit is used to obtain the nonlinear order in the parameter value combination according to the search parameter.

13. The device according to claim 11, characterized in that The DPD modeling module includes: a coefficient solving module, U first search units, and U second search units, where U is an integer greater than 1; The coefficient solving unit is used to update the search parameters of the predistortion model according to the power amplifier input signal and the power amplifier output signal; The first search unit is used to obtain a memory depth value of a signal item in a parameter value combination according to the search parameter; The second search unit is used to obtain a memory depth value of a module value item in the parameter value combination according to the search parameter.

14. A power amplifier, characterized in that: include: An apparatus for executing the method according to any one of claims 1 to 9, or a pre-distortion model selection apparatus according to claim 10, or an apparatus for pre-distortion model selection according to any one of claims 11 to 13.

15. A base station, characterized in that: Comprising a power amplifier as claimed in claim 14.

16. A base station, characterized in that: include: one or more processors; as well as, One or more memories coupled to the one or more processors and storing instructions thereon, which, when the instructions are executed individually or collectively by the one or more processors, cause the base station to perform the method according to any one of claims 1-9.

17. A non-transitory computer-readable storage medium storing machine-executable instructions, characterized in that: When the machine executable instructions are executed individually or collectively by one or more processors of the machine, the machine is caused to perform the method of any one of claims 1-9.

18. A computer program product comprising machine executable instructions, characterized in that When the machine executable instructions are executed individually or collectively by one or more processors of the machine, the machine is caused to perform the method of any one of claims 1-9.

19. A chip system, comprising a circuit system, characterized in that: The circuit system is configured to perform the method according to any one of claims 1-9.

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