Power amplifier design method based on feasible region shrinkage Bayesian optimization

A design method and optimization design technology, applied in design optimization/simulation, based on specific mathematical models, calculations, etc., can solve problems such as long optimization time, objective function spends a lot of time, money and human resources, uncertainty, etc. The optimization process is flexible, balances exploration and development, and improves the effect of convergence speed
CN114297925APending Publication Date: 2022-04-08HANGZHOU DIANZI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HANGZHOU DIANZI UNIV
Publication Date
2022-04-08

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Abstract

The invention discloses a feasible region shrinkage Bayesian optimization-based power amplifier design method, which comprises the following steps of: firstly, determining a matching network target impedance based on load traction and source traction, and then determining a matching network structure and element parameter values through a Chebyshev low-pass topology method; sampling an initial value by using optimal Latin hypercube sampling to obtain an input sample set, realizing sample point evaluation in a normalized weighting mode, searching an optimal hyper-parameter by using a particle swarm algorithm, maximizing a collection function UCB in a feasible region based on a trained Gaussian process model to obtain a next evaluation point, and continuously iterating to obtain an evaluation result; and the broadband high-efficiency power amplifier is realized. The invention provides a power amplifier design method based on feasible region shrinkage Bayesian optimization for the first time, and the feasible region is shrunk by dynamically changing the parameter value beta, so that the optimization is balanced between a global state and a local state, the convergence speed is increased, the power amplifier layout optimization can be effectively guided, and the power amplifier design is realized.
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Description

technical field

[0001] The invention belongs to the field of machine learning and radio frequency power amplifier design, and in particular relates to a power amplifier design method based on feasible domain contraction Bayesian optimization. Background technique

[0002] The design indicators of power amplifiers, such as output power and efficiency, are mutually restrictive, and satisfying these indicators at the same time requires complex topology design and accurate parameter calculation, and even if the selected parameters conform to "Pareto optimal", once the manufactured The power amplifier does not match the analog design, and the designer must repeat the entire design cycle again, so the design process of the power amplifier is very complicated. The general steps include load pulling and source pulling, designing topology, ensuring circuit stability, component parameter calculation and circuit optimization, etc. The most important step is circuit optimization, and an...

Claims

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