Power amplifier parameter design method and device, storage medium and electronic equipment
By constructing an analytical mechanical model and a multi-objective cost function, the problem of the disconnect between efficiency, linearity and thermal stability in power amplifier design was solved, and the synergistic optimization of global performance was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGXI XINBAITE MICROELECTRONICS CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-04-24
AI Technical Summary
In existing power amplifier designs, the physical coupling between efficiency, linearity, and thermal stability is broken, causing the design results to often fall into local optima and making it difficult to achieve global performance synergy.
An analytical mechanical model is constructed, and the efficiency-linearity-thermal stability relationship of the power amplifier is described by Hamiltonian, Lagrangian or action functional, forming a multi-objective cost function, and the design parameters are iteratively optimized by an optimization algorithm.
It achieves global synergistic optimization of power amplifier performance, improving the overall performance of efficiency, linearity and thermal stability.
Smart Images
Figure CN121920299A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power amplifier design technology, specifically to a power amplifier parameter design method, apparatus, storage medium, and electronic device. Background Technology
[0002] Power amplifiers (PAs) are core components in modern wireless communication systems, and their performance directly affects the system's signal quality, energy efficiency, and operating costs. With the evolution of technologies such as 5G / 6G and satellite communication, communication standards have placed almost stringent comprehensive requirements on PAs: under wideband, high peak-to-average power ratio (PAPR) signals, they must simultaneously achieve high power-added efficiency (PAE), low spectral regeneration (such as ACPR), and excellent linearity (such as EVM), while ensuring thermal reliability under high power density.
[0003] Currently, the design approach for power amplifiers is as follows: First, using circuit simulation software (such as ADS and Cadence) based on the nonlinear model of the transistor, the bias point and matching network are initially determined through parameter scanning and empirical iteration. Subsequently, efficiency optimization, linearity optimization, and thermal design are often treated as relatively independent problems, and are handled using different modeling tools and optimization frameworks. For example, the load-pull method is used to optimize efficiency and output power, predistortion algorithms or waveform engineering are used to improve linearity, and then thermal simulation software is used to analyze the steady-state or transient temperature field separately to ensure reliability.
[0004] However, the separate optimization framework severs the inherent physical coupling between efficiency, linearity and thermodynamics, causing the design results to often get stuck in local optima and making it difficult to achieve true global performance synergy. Summary of the Invention
[0005] This application provides a power amplifier parameter design method, apparatus, storage medium, and electronic device, which can achieve global collaborative optimization of power amplifier performance.
[0006] In a first aspect, embodiments of this application provide a method for designing power amplifier parameters, including: In response to the design request, the performance indicators and constraints of the power amplifier are obtained. The performance indicators include efficiency indicators, linearity indicators, and thermal stability indicators, and the constraints include thermal stability constraints and harmonic distortion constraints. Based on the performance indicators and constraints, an analytical mechanical model describing the power amplifier is constructed, including: constructing a first sub-model describing the efficiency-linearity tradeoff based on the efficiency and linearity indicators; constructing a second sub-model describing the thermodynamic behavior based on the thermal stability indicators and thermal stability constraints; constructing a third sub-model for harmonic management and nonlinear distortion control based on the harmonic distortion constraints; and superimposing or weighting the Hamiltonian, Lagrange quantity, or action functional corresponding to the first, second, and third sub-models to form an analytical mechanical model describing the power amplifier. Based on the analytical mechanics model, a multi-objective cost function is constructed, which is J(θ): J(θ) = α * (1 / PAE) + β * ACPR + γ * max(0, Tj - Tj_safe)² + δ * Harmonics_Penalty, where θ represents all design parameters to be optimized; PAE is the power-added efficiency; ACPR is the adjacent channel power ratio; Tj is the transistor junction temperature obtained by calculation or simulation; Tj_safe is the preset safe junction temperature threshold; max(0, Tj - Tj_safe)² is the thermal stability penalty term; Harmonics_Penalty is the harmonic distortion penalty term; and α, β, γ, δ are preset weighting coefficients. An optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of target design parameters; The target design parameters are output to guide the circuit implementation of the power amplifier.
[0007] In the power amplifier parameter design method provided in this application embodiment, the step of constructing a multi-objective cost function based on the analytical mechanical model includes: From the analytical mechanics model, extract the energy terms, action terms, or dynamic equation terms corresponding to the performance indicators and constraints; The energy term, action term, or kinetic equation term are combined into a scalar multi-objective cost function according to preset weighting coefficients, wherein the value of the multi-objective cost function characterizes the degree of deviation between the overall performance of the power amplifier and the design target.
[0008] In the power amplifier parameter design method provided in this application embodiment, the step of iteratively optimizing the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters includes: Initialize a set of design parameters that satisfy physical realizability constraints as the current design parameters; Based on the analytical mechanics model and the current design parameters, calculate the gradient of the multi-objective cost function; Based on the gradient, the current design parameters are updated under the physical realizability constraint to obtain the updated design parameters; The updated design parameters are used as the current design parameters, and the process of calculating the gradient of the multi-objective cost function based on the analytical mechanics model and the current design parameters is returned until the preset convergence condition is met. The current design parameters are then used as the target design parameters.
[0009] In the power amplifier parameter design method provided in the embodiments of this application, the target design parameters include at least one of transistor size, bias point voltage, current, matching network element value, and thermal management structure parameters.
[0010] Secondly, embodiments of this application provide a power amplifier parameter design apparatus, comprising: The acquisition unit is used to acquire the performance indicators and constraints of the power amplifier in response to a design request. The performance indicators include efficiency indicators, linearity indicators, and thermal stability indicators, and the constraints include thermal stability constraints and harmonic distortion constraints. A construction unit is used to construct an analytical mechanical model describing the power amplifier based on the performance indicators and constraints, including: constructing a first sub-model describing the efficiency-linearity tradeoff based on the efficiency and linearity indicators; constructing a second sub-model describing the thermodynamic behavior based on the thermal stability indicators and thermal stability constraints; constructing a third sub-model for harmonic management and nonlinear distortion control based on the harmonic distortion constraints; and superimposing or weighting the Hamiltonian, Lagrangian, or action functionals corresponding to the first, second, and third sub-models to form an analytical mechanical model describing the power amplifier. A construction unit is used to construct a multi-objective cost function J(θ) based on the analytical mechanical model. This multi-objective cost function J(θ) is: J(θ) = α * (1 / PAE) + β * ACPR + γ * max(0, Tj - Tj_safe)² + δ *Harmonics_Penalty, where θ represents all design parameters to be optimized; PAE is the power-added efficiency; ACPR is the adjacent channel power ratio; Tj is the calculated or simulated transistor junction temperature; Tj_safe is a preset safe junction temperature threshold; max(0, Tj - Tj_safe)² is a thermal stability penalty term; Harmonics_Penalty is a harmonic distortion penalty term; and α, β, γ, and δ are preset weighting coefficients. An iterative unit is used to iteratively optimize the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; The output unit is used to output the target design parameters to guide the circuit implementation of the power amplifier.
[0011] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the power amplifier parameter design method described in any of the preceding claims.
[0012] Fourthly, this application 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 computer program to implement the power amplifier parameter design method described in any of the preceding claims.
[0013] In summary, the power amplifier parameter design method provided in this application includes, in response to a design request, obtaining the performance indicators and constraints of the power amplifier; constructing an analytical mechanical model to describe the power amplifier based on the performance indicators and constraints; constructing a multi-objective cost function based on the analytical mechanical model; iteratively optimizing the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; and outputting the target design parameters to guide the circuit implementation of the power amplifier. This application first obtains the performance indicators and constraints, and then constructs an analytical mechanical model to describe the power amplifier based on the performance indicators and constraints, integrating the traditionally separate design dimensions into a dynamic system that can be described and evolved holistically. Based on this, a multi-objective cost function containing multiple performance indicators is constructed, and an optimization algorithm is used to iteratively optimize the multi-objective cost function, ultimately outputting a set of synergistically optimal target design parameters. This scheme fundamentally overcomes the dilemma of local optima and performance trade-offs caused by traditional separate optimization, achieving global synergistic optimization of power amplifier performance. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1 This is a schematic diagram illustrating an application scenario of the power amplifier parameter design method provided in the embodiments of this application.
[0016] Figure 2 This is a flowchart illustrating the power amplifier parameter design method provided in the embodiments of this application.
[0017] Figure 3This is a schematic diagram of the power amplifier parameter design device provided in the embodiments of this application.
[0018] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0020] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.
[0021] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0022] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustration and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.
[0023] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0024] Currently, the design approach for power amplifiers is as follows: First, using circuit simulation software (such as ADS and Cadence) based on the nonlinear model of the transistor, the bias point and matching network are initially determined through parameter scanning and empirical iteration. Subsequently, efficiency optimization, linearity optimization, and thermal design are often treated as relatively independent problems, and are handled using different modeling tools and optimization frameworks. For example, the load-pull method is used to optimize efficiency and output power, predistortion algorithms or waveform engineering are used to improve linearity, and then thermal simulation software is used to analyze the steady-state or transient temperature field separately to ensure reliability.
[0025] However, the separate optimization framework severs the inherent physical coupling between efficiency, linearity and thermodynamics, causing the design results to often get stuck in local optima and making it difficult to achieve true global performance synergy.
[0026] Based on this, embodiments of this application provide a power amplifier parameter design method, apparatus, storage medium, and electronic device. Specifically, the power amplifier parameter design apparatus can be integrated into an electronic device, which can be a server or a terminal, etc. The terminal can include mobile phones, wearable smart devices, tablets, laptops, and personal computers (PCs), etc. The server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.
[0027] For example, such as Figure 1 As shown, the electronic device can respond to a design request, obtain the performance indicators and constraints of the power amplifier; construct an analytical mechanical model to describe the power amplifier based on the performance indicators and constraints; construct a multi-objective cost function based on the analytical mechanical model; iteratively optimize the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; and output the target design parameters to guide the circuit implementation of the power amplifier.
[0028] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.
[0029] Please see Figure 2 , Figure 2 This is a schematic flowchart of the power amplifier parameter design method provided in an embodiment of this application. The specific flow of the power amplifier parameter design method can be as follows: 101. In response to the design request, obtain the performance specifications and constraints of the power amplifier.
[0030] In this embodiment, the electronic device can receive design requests initiated by a user or an external system. These design requests may include basic specifications such as the target operating frequency band, output power range, supply voltage, process node, and package type.
[0031] In the specific implementation process, the performance indicators and constraints of the power amplifier can be obtained from the preset parameter library, design rule library or user input according to the design request.
[0032] Among them, performance indicators include, but are not limited to, efficiency indicators (such as the target value or maximization requirement of power-added efficiency (PAE), linearity indicators (such as the target upper limit of adjacent channel power ratio (ACPR) and error vector magnitude (EVM)), and thermal stability indicators (such as the maximum allowable junction temperature (Tj_max) and thermal resistance requirements).
[0033] These constraints include, but are not limited to, thermal stability constraints (such as the safe operating range of steady-state and transient temperatures), harmonic distortion constraints (such as the suppression requirements for specific harmonics such as second and third harmonics), and physical realizability constraints (such as the allowable range of transistor size process design rules and the limitations of matching network topology).
[0034] In the embodiments of this application, performance indicators and constraints can be obtained by reading configuration files, receiving input from a graphical user interface (GUI), or importing them from an external design management system via an application programming interface (API).
[0035] 102. Based on the performance indicators and constraints, construct an analytical mechanical model to describe the power amplifier.
[0036] The analytical mechanics model includes, but is not limited to, a first sub-model (efficiency-linearity tradeoff model), a second sub-model (thermodynamic model), and a third sub-model (harmonic management and nonlinear distortion control model). Each sub-model can be flexibly constructed using any one of the three analytical mechanics mathematical frameworks: the Hamiltonian formula, the Lagrange formula, or the action functional formula.
[0037] Specifically, step 102 may include the following steps: 1021. Based on the efficiency index and the linearity index, construct the first sub-model describing the efficiency-linearity trade-off relationship.
[0038] Specifically, if the Hamiltonian formula is used to construct the first sub-model, the key electrical state variables of the power amplifier (such as certain voltages of transistors) are defined as "generalized coordinates," and their corresponding dynamic variables (such as associated currents or charges) are defined as "generalized momentum." Based on this, a Hamiltonian is constructed that is simultaneously associated with power-added efficiency (PAE) and linearity indices (such as ACPR and EVM). The dynamic equations derived from this Hamiltonian can describe the intrinsic coupling relationship in state space as linearity evolves when the operating point is changed to improve efficiency.
[0039] If the first sub-model is constructed using the Lagrange formula, a Lagrange quantity is constructed using the same electrical state variables as "generalized coordinates" and their rate of change as "generalized velocity." This Lagrange quantity is typically designed to contain the difference between a "kinetic energy" term reflecting the dynamic energy of the circuit and a "potential energy" or "dissipation" term reflecting the operating point and nonlinear losses. This Lagrange quantity naturally characterizes the balance between the efficiency optimization process (related to the energy term) and the linearity constraint (related to the nonlinear distortion term).
[0040] If the first sub-model is constructed using the action functional formula, then based on the aforementioned Lagrange quantity, integration over a complete duty cycle or signal period yields the action functional. The value of this action functional characterizes the overall integral effect of efficiency and linearity performance over a given period. The optimization objective is to find design parameters that cause this action functional to approach its extremum (e.g., minimize it), thereby achieving global synergistic optimization of average efficiency and cumulative distortion levels.
[0041] 1022. Based on the thermal stability index and thermal stability constraints, construct a second sub-model to describe the thermodynamic behavior.
[0042] Specifically, if the Hamiltonian formula is used to construct the second sub-model, then thermal state variables (such as the temperature of critical nodes) are defined as "generalized coordinates," and quantities related to heat flow or entropy change are defined as "generalized momentum," thus constructing a thermodynamic Hamiltonian. This Hamiltonian encapsulates the coupling relationship between heat generation (originating from electrical power consumption), heat conduction (through thermal resistance networks), and heat storage (thermal capacity effect) within the chip, and its dynamic equations describe the co-evolution of the temperature field and heat flow.
[0043] If the second sub-model is constructed using the Lagrange formula, then temperature or its distribution is used as the "generalized coordinate," and its rate of change is used as the "generalized velocity" to construct the thermodynamic Lagrange quantity. The construction of this Lagrange quantity can be based on the fundamental laws of heat conduction, making its derived equations of motion equivalent to the system's thermal equilibrium differential equations. Thermal stability constraints (such as the maximum junction temperature limit) can be directly embedded into this model as boundary conditions or constraint terms.
[0044] If a second sub-model is constructed using the action functional formula, then integrating the aforementioned thermodynamic Lagrangian quantities over time and possibly spatial dimensions yields the thermodynamic action functional. The extremum principle of this action functional corresponds to the optimal (or actual) spatiotemporal temperature distribution that satisfies initial conditions, boundary conditions, and reliability constraints. By optimizing this action, global management of transient and steady-state thermal behavior can be achieved.
[0045] 1023. Based on harmonic distortion constraints, construct a third sub-model for harmonic management and nonlinear distortion control.
[0046] Specifically, if the Hamiltonian formula is used to construct the third sub-model, the variables related to the amplitude and phase information of each harmonic component are defined as the system's "generalized coordinates" and "generalized momentum," thus constructing the harmonic Hamiltonian. This Hamiltonian aims to maximize the desired fundamental wave energy while suppressing the energy of undesired harmonics, and its equation describes the nonlinear dynamics of energy exchange between different harmonic modes.
[0047] If the third sub-model is constructed using the Lagrange formula, then the amplitude or phase of the harmonic components are used as "generalized coordinates" to construct the harmonic Lagrange quantity. This Lagrange quantity can be expanded in the frequency domain using the characteristics of nonlinear devices to form energy terms related to the generation and suppression of each harmonic, thus providing a mathematical description for waveform shaping.
[0048] If the third sub-model is constructed using the action functional formula (a typical approach), then an "action functional" directly related to the output time-domain waveform is defined. This action functional is obtained by integrating over time a "Lagrange density" function that aims to reward efficient fundamental wave conversion and penalize higher harmonic content. Optimizing the design parameters to minimize this action functional essentially involves global optimization of the output waveform, automatically finding the circuit operating conditions that achieve optimal harmonic suppression and linearity improvement.
[0049] 1024. Integrate the first sub-model, the second sub-model, and the third sub-model into an analytical mechanical model to describe the power amplifier.
[0050] Specifically, within a unified analytical mechanics mathematical framework, the first sub-model, the second sub-model, and the third sub-model can be combined to form an analytical mechanics model for describing the power amplifier.
[0051] Understandably, the three sub-models are constructed by uniformly selecting one of the three analytical mechanics mathematical frameworks mentioned above, and can ultimately be transformed into a scalar value that characterizes the performance contribution of the sub-model (e.g., the value of the Hamiltonian or Lagrange quantity in the typical state, or the value of the action functional itself).
[0052] In subsequent integration, these scalar values can be combined into an analytical mechanical model describing the power amplifier by superposition or weighted summation. That is, the Hamiltonians, Lagrangians, or action functionals corresponding to the first, second, and third sub-models can be superimposed or weighted summed to form an analytical mechanical model describing the power amplifier.
[0053] 103. Construct a multi-objective cost function based on an analytical mechanics model.
[0054] It is understood that this embodiment aims to transform the analytical mechanics model into a multi-objective cost function that can be used for numerical optimization, thereby formalizing the multi-performance index collaborative optimization design problem into a solvable mathematical optimization problem.
[0055] In this embodiment of the application, the construction of a multi-objective cost function based on an analytical mechanics model specifically includes the following steps: 1031. Extract the energy terms, action terms, or dynamic equation terms corresponding to the performance indicators and constraints from the analytical mechanics model.
[0056] Specifically, components corresponding to various performance indicators (efficiency, linearity, thermal stability) and constraints (harmonic distortion, physical realizability) can be directly identified and extracted from the analytical mechanics model.
[0057] For example, if the analytical mechanics model is in Hamiltonian form, the extracted terms are the energy terms related to the various performance indicators and constraints. If the analytical mechanics model is in Lagrangian form, the extracted terms are the dynamic equation terms related to the various performance indicators and constraints. Their physical meaning is similar to the energy terms in Hamiltonian, but the expression is different. If the analytical mechanics model is based on action functionals, the extracted terms are the action terms related to the performance of the various performance indicators and constraints over the entire time (or frequency) integration interval.
[0058] 1032. Combine the energy term, action term, or dynamic equation term into a scalar multi-objective cost function according to preset weighting coefficients, where the value of the multi-objective cost function characterizes the degree of deviation between the overall performance of the power amplifier and the design objective.
[0059] In this embodiment, based on the degree of conformity (positive or negative correlation) between the energy term, action term, or kinetic equation term and the design objective, corresponding preset weighting coefficients can be assigned, and a multi-objective cost function can be constructed through linear weighting or other mathematical combinations. Each weighting coefficient reflects the priority of the corresponding performance indicator or constraint in the design objective.
[0060] For example, the multi-objective cost function J(θ) can be expressed as follows: J(θ) = α * (1 / PAE) + β * ACPR + γ * max(0, Tj - Tj_safe)² + δ *Harmonics_Penalty.
[0061] Where θ represents all design parameters to be optimized; PAE is power-added efficiency; ACPR is adjacent channel power ratio; Tj is the transistor junction temperature obtained by calculation or simulation; Tj_safe is the preset safe junction temperature threshold; max(0, Tj - Tj_safe)² is the thermal stability penalty term; Harmonics_Penalty is the harmonic distortion penalty term; α,β,γ,δ are preset weighting coefficients that can be adjusted according to the priority of different design stages.
[0062] The value of this multi-objective cost function quantitatively characterizes the degree of deviation between the overall performance of the power amplifier and the design objectives under the current design parameters. A smaller value indicates that the overall performance is closer to or better than the design objectives; a larger value indicates a greater deviation.
[0063] 104. An optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of target design parameters.
[0064] First, a set of design parameters that satisfy physical realizability constraints can be initialized as the current design parameters. Then, based on the analytical mechanics model and the current design parameters, the gradient of the multi-objective cost function is calculated. Next, based on the gradient, the current design parameters are updated under the physical realizability constraints to obtain the updated design parameters. Finally, the updated design parameters are used as the current design parameters, and the process of calculating the gradient of the multi-objective cost function based on the analytical mechanics model and the current design parameters is returned until the preset convergence condition is met, at which point the current design parameters are used as the target design parameters.
[0065] The initialization method can be random initialization, experience-based initialization, or rule-based initialization, and the specific method can be selected according to the actual situation.
[0066] In this embodiment, the gradient refers to the direction in which the multi-objective cost function increases the fastest at the current design parameter point. Therefore, its opposite direction (the negative gradient direction) is the local direction in which the value of the multi-objective cost function decreases the fastest. Since analytical mechanics models are usually composed of differentiable mathematical expressions, their gradient calculations can be efficiently and accurately completed using the automatic differentiation function of modern deep learning frameworks (such as TensorFlow and PyTorch), without the need for cumbersome numerical differencing.
[0067] In the specific implementation process, gradient-based optimization algorithms (such as gradient descent, conjugate gradient, quasi-Newton method, etc.) can be used to update the current design parameters according to the direction of the gradient, and ensure that the design parameters after each update meet the physical realizability constraints.
[0068] Then, the gradient calculation and update steps are repeated until the change in the multi-objective cost function is less than a preset threshold, or the maximum number of iterations is reached. At this point, the current design parameters are the optimized set of target design parameters. These target design parameters may include, but are not limited to, transistor size, bias point voltage, current, matching network component values, and thermal management structure parameters.
[0069] 105. Output target design parameters to guide the circuit implementation of the power amplifier.
[0070] At this point, the target design parameters can be output in a file format that can be directly used in the downstream design process.
[0071] For example, it generates standard circuit netlist files (such as SPICE netlist) containing optimized component values. It outputs a design report detailing transistor dimensions, bias conditions, matched network parameters, and thermal design recommendations. The target design parameters can be directly imported into electronic design automation (EDA) tools (such as Cadence Virtuoso and ADS) for automatic schematic generation, driver layout design, or subsequent signature verification simulations.
[0072] In summary, the power amplifier parameter design method provided in this application includes, in response to a design request, obtaining the performance indicators and constraints of the power amplifier; constructing an analytical mechanical model to describe the power amplifier based on the performance indicators and constraints; constructing a multi-objective cost function based on the analytical mechanical model; iteratively optimizing the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; and outputting the target design parameters to guide the circuit implementation of the power amplifier. This application embodiment can achieve global collaborative optimization of multiple dimensions of power amplifier performance, such as efficiency, linearity, and thermal stability. Specifically, this application embodiment first obtains the performance indicators and constraints, and then constructs an analytical mechanical model to describe the power amplifier based on the performance indicators and constraints, integrating the traditionally separate design dimensions into a dynamic system that can be described and evolved holistically. Based on this, a multi-objective cost function containing multiple performance indicators is constructed, and an optimization algorithm is used to iteratively optimize the multi-objective cost function, finally outputting a set of collaboratively optimal target design parameters. This scheme fundamentally overcomes the dilemma of local optima and performance trade-offs caused by traditional separate optimization, achieving global collaborative optimization of power amplifier performance.
[0073] To facilitate better implementation of the power amplifier parameter design method provided in this application, this application also provides a power amplifier parameter design apparatus. The meanings of the terms used are the same as in the power amplifier parameter design method described above, and specific implementation details can be found in the descriptions within the method embodiments.
[0074] Please see Figure 3 , Figure 3 This is a schematic diagram of the power amplifier parameter design device provided in an embodiment of this application. The power amplifier parameter design device may include an acquisition unit 201, a construction unit 202, a building unit 203, an iteration unit 204, and an output unit 205. Acquisition unit 201 is used to acquire the performance indicators and constraints of the power amplifier in response to a design request; Building unit 202 is used to build an analytical mechanical model to describe the power amplifier based on performance indicators and constraints; Construction unit 203 is used to construct a multi-objective cost function based on an analytical mechanics model; Iteration unit 204 is used to iteratively optimize the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; Output unit 205 is used to output target design parameters to guide the circuit implementation of the power amplifier.
[0075] For specific implementation methods of each of the above units, please refer to the embodiments of the power amplifier parameter design method described above, which will not be repeated here.
[0076] In summary, the power amplifier parameter design apparatus provided in this application embodiment can acquire the performance indicators and constraints of the power amplifier in response to a design request via an acquisition unit 201; construct an analytical mechanical model describing the power amplifier based on the performance indicators and constraints via a construction unit 202; construct a multi-objective cost function based on the analytical mechanical model via a construction unit 203; iterate the multi-objective cost function using an optimization algorithm via an iteration unit 204 to obtain a set of target design parameters; and output the target design parameters via an output unit 205 to guide the circuit implementation of the power amplifier. This application embodiment first acquires the performance indicators and constraints, and then constructs an analytical mechanical model describing the power amplifier based on these indicators and constraints, integrating the traditionally separate design dimensions into a dynamic system that can be described and evolved holistically. Based on this, a multi-objective cost function containing multiple performance indicators is constructed, and an optimization algorithm is used to iteratively optimize the multi-objective cost function, ultimately outputting a set of synergistically optimal target design parameters. This scheme fundamentally overcomes the dilemma of local optima and performance trade-offs caused by traditional separate optimization, achieving global synergistic optimization of power amplifier performance.
[0077] This application also provides an electronic device that may integrate the power amplifier parameter design device of this application, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.
[0078] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on the use of the electronic device. In addition, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
[0079] Although not shown, the electronic device may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302 to realize various functions, as follows: In response to design requests, obtain the performance specifications and constraints of the power amplifier; Based on performance indicators and constraints, an analytical mechanical model is constructed to describe the power amplifier. Constructing a multi-objective cost function based on an analytical mechanics model; An optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of target design parameters; Output target design parameters to guide the circuit implementation of the power amplifier.
[0080] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0081] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps: In response to design requests, obtain the performance specifications and constraints of the power amplifier; Based on performance indicators and constraints, an analytical mechanical model is constructed to describe the power amplifier. Constructing a multi-objective cost function based on an analytical mechanics model; An optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of results; Output target design parameters to guide the circuit implementation of the power amplifier.
[0082] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.
[0083] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0084] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.
[0085] The above provides a detailed description of the power amplifier parameter design method, apparatus, storage medium, and electronic device provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for designing power amplifier parameters, characterized in that, include: In response to the design request, the performance indicators and constraints of the power amplifier are obtained. The performance indicators include efficiency indicators, linearity indicators, and thermal stability indicators, and the constraints include thermal stability constraints and harmonic distortion constraints. Based on the performance indicators and constraints, an analytical mechanical model describing the power amplifier is constructed, including: constructing a first sub-model describing the efficiency-linearity tradeoff based on the efficiency and linearity indicators; constructing a second sub-model describing the thermodynamic behavior based on the thermal stability indicators and thermal stability constraints; constructing a third sub-model for harmonic management and nonlinear distortion control based on the harmonic distortion constraints; and superimposing or weighting the Hamiltonian, Lagrange quantity, or action functional corresponding to the first, second, and third sub-models to form an analytical mechanical model describing the power amplifier. Based on the analytical mechanics model, a multi-objective cost function is constructed, which is J(θ): J(θ) = α * (1 / PAE) + β * ACPR + γ * max(0, Tj - Tj_safe)² + δ * Harmonics_Penalty, where θ represents all design parameters to be optimized; PAE is the power-added efficiency; ACPR is the adjacent channel power ratio; Tj is the transistor junction temperature obtained by calculation or simulation; Tj_safe is the preset safe junction temperature threshold; max(0, Tj - Tj_safe)² is the thermal stability penalty term; Harmonics_Penalty is the harmonic distortion penalty term; and α, β, γ, δ are preset weighting coefficients. An optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of target design parameters; The target design parameters are output to guide the circuit implementation of the power amplifier.
2. The power amplifier parameter design method as described in claim 1, characterized in that, The construction of the multi-objective cost function based on the analytical mechanics model includes: From the analytical mechanics model, extract the energy terms, action terms, or dynamic equation terms corresponding to the performance indicators and constraints; The energy term, action term, or kinetic equation term are combined into a scalar multi-objective cost function according to preset weighting coefficients, wherein the value of the multi-objective cost function characterizes the degree of deviation between the overall performance of the power amplifier and the design target.
3. The power amplifier parameter design method as described in claim 1, characterized in that, The optimization algorithm is used to iteratively optimize the multi-objective cost function to obtain a set of target design parameters, including: Initialize a set of design parameters that satisfy physical realizability constraints as the current design parameters; Based on the analytical mechanics model and the current design parameters, calculate the gradient of the multi-objective cost function; Based on the gradient, the current design parameters are updated under the physical realizability constraint to obtain the updated design parameters; The updated design parameters are used as the current design parameters, and the process of calculating the gradient of the multi-objective cost function based on the analytical mechanics model and the current design parameters is returned until the preset convergence condition is met. The current design parameters are then used as the target design parameters.
4. The power amplifier parameter design method according to any one of claims 1-3, characterized in that, The target design parameters include at least one of transistor size, bias point voltage, current, matching network element values, and thermal management structure parameters.
5. A power amplifier parameter design device, characterized in that, include: The acquisition unit is used to acquire the performance indicators and constraints of the power amplifier in response to a design request. The performance indicators include efficiency indicators, linearity indicators, and thermal stability indicators, and the constraints include thermal stability constraints and harmonic distortion constraints. A construction unit is used to construct an analytical mechanical model describing the power amplifier based on the performance indicators and constraints, including: constructing a first sub-model describing the efficiency-linearity tradeoff based on the efficiency and linearity indicators; constructing a second sub-model describing the thermodynamic behavior based on the thermal stability indicators and thermal stability constraints; constructing a third sub-model for harmonic management and nonlinear distortion control based on the harmonic distortion constraints; and superimposing or weighting the Hamiltonian, Lagrangian, or action functionals corresponding to the first, second, and third sub-models to form an analytical mechanical model describing the power amplifier. A construction unit is used to construct a multi-objective cost function J(θ) based on the analytical mechanical model. This multi-objective cost function J(θ) is: J(θ) = α * (1 / PAE) + β * ACPR + γ * max(0, Tj - Tj_safe)² + δ *Harmonics_Penalty, where θ represents all design parameters to be optimized; PAE is the power-added efficiency; ACPR is the adjacent channel power ratio; Tj is the calculated or simulated transistor junction temperature; Tj_safe is a preset safe junction temperature threshold; max(0, Tj - Tj_safe)² is a thermal stability penalty term; Harmonics_Penalty is a harmonic distortion penalty term; and α, β, γ, and δ are preset weighting coefficients. An iterative unit is used to iteratively optimize the multi-objective cost function using an optimization algorithm to obtain a set of target design parameters; The output unit is used to output the target design parameters to guide the circuit implementation of the power amplifier.
6. A storage medium, characterized in that, The storage medium stores multiple instructions, which are adapted for loading by a processor to execute the power amplifier parameter design method according to any one of claims 1-4.
7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power amplifier parameter design method as described in any one of claims 1-4.