A method and apparatus for updating digital predistortion coefficients for a frequency hopping communication system

Abstract

The application provides a digital pre-distortion coefficient updating method and device for a frequency hopping communication system, comprising: obtaining a historical digital pre-distortion coefficient corresponding to a system frequency hopping to a target frequency point, and collecting training data in a starting stage of a current hop; obtaining an intermediate vector for representing a system change degree through an adaptive filtering algorithm based on the historical digital pre-distortion coefficient and the training data, and determining a change measure based on the intermediate vector; comparing the change measure with a preset threshold to divide system state changes into different levels, and dynamically triggering a matching updating mode; wherein the updating mode comprises a keeping mode, a light tracking mode and an accurate reconstruction mode. The application quantitatively evaluates a change measure of a coefficient updating vector, divides system state changes into different levels, and dynamically triggers the most matching updating mode, so as to realize global optimization of system performance and resource consumption.

Description

Technical Field

[0001] This application belongs to the field of communication system technology, and in particular relates to a method and apparatus for updating digital predistortion coefficients for frequency hopping communication systems. Background Technology

[0002] Digital predistortion (DPD) is a key technology for compensating for power amplifier nonlinearity and suppressing spectral regeneration. In frequency-hopping systems, the power amplifier characteristics not only change with frequency switching, but also dynamically drift due to factors such as its own operating state, ambient temperature, and even device aging. Therefore, the system not only needs to store the corresponding DPD coefficients for different frequency points, but also requires a mechanism to update these coefficients in a timely and accurate manner to track the aforementioned dynamic changes.

[0003] Existing frequency-hopping DPD coefficient update techniques mainly rely on fixed-policy update methods based on timed or event-triggered updates. These methods lack fine-grained awareness of the system state, leading to two key drawbacks:

[0004] 1) Unnecessary updates cause system jitter: Whether it's a simple per-hop update or a fixed periodic update, the update decision is independent of the system state. This method cannot distinguish the essential difference between spurious disturbances caused by measurement noise and algorithmic randomness and the real changes in power amplifier characteristics, resulting in the DPD coefficient continuously and randomly jittering around the theoretical optimal solution. This unnecessary update not only does not improve performance but also introduces a disturbance source into the system, deteriorating the stability and linearization performance of the communication link.

[0005] 2) Fixed algorithms struggle to adapt to dynamic scenarios: Existing solutions typically employ a single update algorithm, which cannot adapt to dynamic working environments. For example, the LMS algorithm is computationally simple but converges slowly, exhibits significant jitter, and cannot accurately identify data; while algorithms like RLS, although highly accurate, are computationally complex, and their high overhead becomes an unnecessary waste when the system is in a steady state. In fact, the system's requirements differ at different times: during steady-state perturbations, the focus is on suppressing fluctuations; during normal changes, efficient tracking is required; and during drastic changes, accurate reconstruction is necessary. Clearly, no fixed algorithm can achieve global optimization across the three mutually constraining dimensions of stability, convergence speed, and computational efficiency.

[0006] In summary, existing technologies are limited by a single and blind update mechanism, making it difficult to achieve optimal performance in complex real-world environments, which constitutes a bottleneck for further improving the performance of frequency-hopping DPD systems. Summary of the Invention

[0007] In view of this, this application aims to provide a digital predistortion coefficient update method and apparatus for frequency hopping communication systems to solve at least one of the above-mentioned problems.

[0008] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0009] In a first aspect, this application provides a method for updating digital predistortion coefficients in a frequency-hopping communication system, comprising:

[0010] Obtain the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and collect training data at the beginning stage of the current hop;

[0011] Based on the historical digital predistortion coefficients and the training data, an intermediate vector is obtained through an adaptive filtering algorithm to characterize the degree of system change, and a change metric is determined based on the intermediate vector.

[0012] The change metric is compared with a preset threshold to classify system state changes into different levels and dynamically trigger a matching update mode; wherein the update mode includes hold mode, lightweight tracking mode and precise reconstruction mode.

[0013] Secondly, based on the same inventive concept, this application also provides a digital predistortion coefficient update device for a frequency hopping communication system, comprising:

[0014] The data acquisition module is configured to acquire the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and to acquire training data at the beginning of the current hop.

[0015] The change metric determination module is configured to obtain an intermediate vector characterizing the degree of system change through an adaptive filtering algorithm based on the historical digital predistortion coefficients and the training data, and to determine the change metric based on the intermediate vector.

[0016] The coefficient update module is configured to compare the change metric with a preset threshold to classify system state changes into different levels and dynamically trigger a matching update mode; wherein the update mode includes hold mode, lightweight tracking mode and precise reconstruction mode.

[0017] Thirdly, based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a digital predistortion coefficient update method for a frequency hopping communication system as described in the first aspect.

[0018] Fourthly, based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute a digital predistortion coefficient update method for a frequency hopping communication system as described in the first aspect.

[0019] Compared with the prior art, the digital predistortion coefficient update method and apparatus for frequency hopping communication systems described in this application have the following advantages:

[0020] The digital predistortion coefficient update method for frequency hopping communication systems described in this application combines state awareness, multi-threshold decision-making, and multi-mode update, realizing intelligent, hierarchical, and resource-optimized digital predistortion coefficient update in frequency hopping communication systems. It fundamentally solves the bottleneck problem of difficulty in balancing stability, tracking speed, and computational efficiency in traditional methods. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a flowchart of a digital predistortion coefficient update method for a frequency hopping communication system according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of a digital predistortion coefficient update device for a frequency hopping communication system according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of the hardware structure of the electronic device described in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0026] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0027] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0028] Please see Figure 1 As shown, this embodiment provides a method for updating digital predistortion coefficients in a frequency hopping communication system, specifically including the following steps:

[0029] Step S101: Obtain the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and collect training data at the beginning stage of the current hop.

[0030] Specifically, in this embodiment, the system hops to the target frequency and retrieves the historical digital predistortion (DPD) coefficients corresponding to that frequency from the memory. And collect a piece of training data at the beginning of the current jump.

[0031] Step S102: Based on historical digital predistortion coefficients and training data, an intermediate vector is obtained through an adaptive filtering algorithm to characterize the degree of system change, and the change metric is determined based on the intermediate vector.

[0032] Specifically, in this embodiment, based on historical digital predistortion coefficients and training data, a low-complexity adaptive filtering algorithm is used to calculate an intermediate vector characterizing the degree of system variation. For example, a low-complexity variant of the affine projection algorithm can be used to obtain... And solve for the change measure of the intermediate vector. Change measurement It is usually the L2 norm or weighted L2 norm of the vector.

[0033] Furthermore, using historical data predistortion coefficients Using the collected training data as initial values, an input matrix is ​​constructed. and expected response vector First, calculate the prior error vector. :

[0034] ;

[0035] The coefficient update vector is calculated using the core equations of the affine projection algorithm (APA). :

[0036] ;

[0037] In the formula, Here is the regularization constant. It is an identity matrix.

[0038] It is important to note that because the input matrix of the DPD model is composed of its high-order nonlinear terms, it disrupts the Tollitz structure upon which traditional fast affine projection algorithms rely. Therefore, the low-complexity affine projection algorithm referred to in this embodiment essentially refers to a class of general low-complexity methods capable of efficiently solving the core equations of affine projection. These methods include, but are not limited to, the conjugate gradient CG algorithm, or other iterative algorithms suitable for solving symmetric positive definite matrices.

[0039] The change measure of the coefficient update vector defined in this embodiment The L2 norm of the vector can be chosen as follows:

[0040] .

[0041] Step S103: Compare the change measurement with a preset threshold to classify system state changes into different levels and dynamically trigger the corresponding update mode; wherein, the update mode includes hold mode, lightweight tracking mode and precise reconstruction mode.

[0042] Specifically, in this embodiment, the change measurement With the preset first threshold Second threshold (in: Compare the calculated change measures. With the preset first threshold Second threshold Perform real-time comparisons and switch between three working modes according to the following rules.

[0043] Among them, the first threshold The settings should be related to the system background noise level, for example: , This is an estimate of the noise power. The system tolerance coefficient; the second threshold. It should be correlated with the statistical characteristics of historical update volume, for example: , This is a coefficient set based on the system sensitivity.

[0044] By calculating the change measure of the coefficient update vector in real time and comparing it with multiple thresholds, the system can autonomously select the optimal working mode, realizing a fundamental shift from passive execution to intelligent decision-making and significantly improving the system's autonomous intelligence level.

[0045] Based on the comparison results, execute one of the following three update modes:

[0046] (1) Maintaining the pattern: when When the system determines that the current change is only a noise disturbance, it enters hold mode.

[0047] In this mode, the system discards the intermediate vectors calculated this time. Maintain historical digital predistortion coefficient Completely unchanged and continue to be used. Process all subsequent data for this jump. This process does not perform any coefficient update operations, thus achieving zero computational overhead while maintaining optimal system stability.

[0048] (2) Lightweight Tracking Mode: When When the system determines that the power amplifier characteristics have changed moderately and tracking is required, it enters the lightweight tracking mode.

[0049] In this mode, this embodiment uses a first-class adaptive filtering algorithm with low computational complexity to update the coefficients by introducing a step size factor. Execution coefficient update:

[0050] ;

[0051] After the update, immediately It is applied to the pre-distortion processing of all subsequent data after the current jump, enabling real-time and efficient tracking.

[0052] It should be noted that the first type of adaptive filtering algorithm in this embodiment can also adopt the normalized least mean square (NLMS) algorithm or other low-complexity algorithms, but the affine projection algorithm and its variants are the best choice for implementing this application because of their fast convergence speed and strong noise resistance.

[0053] (3) Precise reconstruction mode: when When a drastic change occurs, such as a sudden temperature change or device aging, the system enters the precise reconstruction mode.

[0054] At this point, a new set of DPD coefficients is recalculated using the second type of coefficient calculation algorithm, which has higher computational complexity but higher accuracy. And store it in the memory. To ensure communication reliability, Stored for immediate use on the next access; the current jump continues to use the original coefficient. Processing complete. This mode ensures the system continues to operate reliably even under extreme conditions.

[0055] This embodiment calculates a novel DPD coefficient using a recursive least squares method based on QR decomposition. The following explanation will be provided as an example:

[0056] The input matrix is ​​obtained by recursively calculating each sample point from the collected frame of data. autocorrelation matrix QR decomposition form:

[0057] ;

[0058] In the formula, It is an orthogonal matrix. It is an upper triangular matrix. Simultaneously, the input matrix is ​​obtained. With the expected response vector The cross-correlation vector formed corresponding transformation vector ,satisfy:

[0059] ;

[0060] This allows us to obtain the coefficient vector for each iteration. .because Since it is an upper triangular matrix, there is no need to directly solve for the inverse matrix; it can be obtained through back substitution. When the iteration reaches a certain number of times... Convergence is achieved, and the convergence value is recorded as the new DPD coefficient. .

[0061] This embodiment achieves intelligent scheduling of system resources by establishing a three-level decision-making mechanism of hold mode, lightweight tracking mode, and precise reconstruction mode. This method can significantly save computing power in hold mode, achieve efficient tracking with balanced overhead in lightweight tracking mode, and precisely allocate computing resources in precise reconstruction mode, based on the actual changes in power amplifier characteristics. This results in synergistic optimization of stability, adaptability, and computational efficiency at the system level. The three operating modes form an organic whole, producing a significant synergistic enhancement effect. This architecture covers the entire operating scenario from steady-state perturbations to drastic changes, and its overall technical effect surpasses the simple superposition of the modes, forming an organically unified solution. This innovation fundamentally breaks through the bottleneck of mutual constraint between stability and agility in existing technologies, opening up a new technical path for improving the linearization performance of frequency-hopping communication systems.

[0062] In this embodiment, step S103 involves quantifying the measurement of the evaluation coefficient update vector, dividing the system state changes into different levels, and dynamically triggering the update mode that best matches them, thereby achieving global optimization of system performance and resource consumption.

[0063] It should be noted that when the system accesses a frequency point for the first time, it can be forced into precise reconstruction mode, and the initial coefficients can be calculated using the first-hop data through RLS or batch LS algorithm.

[0064] The method described in this application can be executed by a DSP, FPGA, or ASIC and integrated into a frequency hopping radio or base station, significantly improving its linearization performance and operational efficiency.

[0065] The method described in this application combines state awareness, multi-threshold decision-making, and multi-mode updating, realizing intelligent, hierarchical, and resource-optimized digital predistortion coefficient updating in frequency hopping communication systems. It fundamentally solves the bottleneck problem of difficulty in balancing stability, tracking speed, and computational efficiency in traditional methods.

[0066] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, embodiments of this application also provide a digital predistortion coefficient update device for a frequency hopping communication system.

[0068] like Figure 2 As shown, the digital predistortion coefficient update device for a frequency hopping communication system includes:

[0069] The data acquisition module 11 is configured to acquire the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and to acquire training data at the beginning of the current hop.

[0070] The change metric determination module 12 is configured to obtain an intermediate vector characterizing the degree of change in the system through an adaptive filtering algorithm based on historical digital predistortion coefficients and training data, and to determine the change metric based on the intermediate vector.

[0071] The coefficient update module 13 is configured to compare the change metric with a preset threshold to classify system state changes into different levels and dynamically trigger the corresponding update mode; the update modes include hold mode, lightweight tracking mode and precise reconstruction mode.

[0072] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.

[0073] The apparatus of the above embodiments is used to implement the corresponding method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0074] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the methods described in any of the above embodiments.

[0075] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0076] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0077] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0078] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0079] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0080] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0081] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0082] The electronic devices described above are used to implement the corresponding methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0083] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the methods described in any of the above embodiments.

[0084] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0085] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to perform the methods described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0086] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0087] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0088] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for updating digital predistortion coefficients in a frequency-hopping communication system, characterized in that, include: Obtain the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and collect training data at the beginning stage of the current hop; Based on the historical digital predistortion coefficients and the training data, an intermediate vector characterizing the degree of system change is obtained through an adaptive filtering algorithm, and a change metric is determined based on the intermediate vector; including: Using the historical digital predistortion coefficients as initial values, a coefficient update vector is obtained based on the training data through an affine projection algorithm, and a change measure is determined based on the L2 norm of the coefficient update vector. The change metric is compared with a preset threshold to classify system state changes into different levels and dynamically trigger a matching update mode; wherein, the update mode includes hold mode, lightweight tracking mode, and precise reconstruction mode; including: The change metric is compared in real time with a first threshold and a second threshold, wherein the first threshold is associated with the system background noise level, the second threshold is associated with the statistical characteristics of historical update volume, and the second threshold is greater than the first threshold; when the change metric is less than or equal to the first threshold, the coefficient update vector is discarded and the historical digital predistortion coefficients are maintained to process the current hop data; when the change metric is greater than the first threshold and less than or equal to the second threshold, a step size factor is introduced, and an incremental update coefficient is obtained based on the historical digital predistortion coefficients and the coefficient update vector, and the incremental update coefficients are immediately used for predistortion processing of subsequent data of the current hop after the update; when the change metric is greater than the second threshold, a new digital predistortion coefficient is recalculated using a second type of coefficient calculation algorithm and stored in memory for use on the next access, and the current hop continues to use the historical digital predistortion coefficients for processing.

2. The digital predistortion coefficient update method for a frequency hopping communication system according to claim 1, characterized in that: The input matrix and the expected response vector are constructed using the training data, and the prior error vector is obtained based on the historical digital predistortion coefficients. Based on the prior error vector, the coefficient update vector is calculated using an affine projection algorithm, and the change measure is obtained based on the coefficient update vector.

3. The digital predistortion coefficient update method for a frequency hopping communication system according to claim 1, characterized in that: The first threshold is determined based on the system's noise power estimate and the system tolerance coefficient; the second threshold is determined based on the statistical characteristics of historical update data and the system sensitivity setting coefficient.

4. A method for updating digital predistortion coefficients in a frequency-hopping communication system according to claim 1 or 2, characterized in that: The affine projection algorithm employs either the conjugate gradient algorithm or an iterative solution algorithm applicable to symmetric positive definite matrices.

5. The digital predistortion coefficient update method for a frequency hopping communication system according to claim 1, characterized in that: The second type of coefficient calculation algorithm is a recursive least squares method based on QR decomposition. It recursively obtains the coefficient vector by taking each sample point of the collected frame of data, and uses the coefficient vector that reaches convergence as the new digital predistortion coefficient.

6. The digital predistortion coefficient update method for a frequency hopping communication system according to claim 4, characterized in that: When the system accesses the target frequency for the first time, it is forced to enter the precise reconstruction mode and uses the first-hop data to calculate the initial digital predistortion coefficients through a recursive least squares algorithm or a batch least squares algorithm.

7. A digital predistortion coefficient update device for a frequency hopping communication system, characterized in that, include: The data acquisition module is configured to acquire the historical digital predistortion coefficients corresponding to the system frequency hopping to the target frequency point, and to acquire training data at the beginning of the current hop. A change metric determination module is configured to obtain an intermediate vector characterizing the degree of system change using an adaptive filtering algorithm based on the historical digital predistortion coefficients and the training data, and to determine a change metric based on the intermediate vector; including: Using the historical digital predistortion coefficients as initial values, a coefficient update vector is obtained based on the training data through an affine projection algorithm, and a change measure is determined based on the L2 norm of the coefficient update vector. The coefficient update module is configured to compare the change metric with a preset threshold to classify system state changes into different levels and dynamically trigger matching update modes; wherein, the update modes include hold mode, lightweight tracking mode, and precise reconstruction mode; including: The change metric is compared in real time with a first threshold and a second threshold, wherein the first threshold is associated with the system background noise level, the second threshold is associated with the statistical characteristics of historical update volume, and the second threshold is greater than the first threshold; when the change metric is less than or equal to the first threshold, the coefficient update vector is discarded and the historical digital predistortion coefficients are maintained to process the current hop data; when the change metric is greater than the first threshold and less than or equal to the second threshold, a step size factor is introduced, and an incremental update coefficient is obtained based on the historical digital predistortion coefficients and the coefficient update vector, and the incremental update coefficients are immediately used for predistortion processing of subsequent data of the current hop after the update; when the change metric is greater than the second threshold, a new digital predistortion coefficient is recalculated using a second type of coefficient calculation algorithm and stored in memory for use on the next access, and the current hop continues to use the historical digital predistortion coefficients for processing.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a digital predistortion coefficient update method for a frequency hopping communication system as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that, in, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the digital predistortion coefficient update method for a frequency hopping communication system as described in any one of claims 1-6.

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