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behavioral modeling of RF power amplifiers using a time-domain characterization approach

INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2025年

作者： Ortali, J. Mons, S. Reveyr, T. Ngoya, E. Maziere, C. CNRS XLIM 123 Ave Albert Thomas Limoges France AMCAD Engn 20 Rue Atlantis Limoges France

In this article, we discuss the behavioral modeling of the power amplifier (PA) for system-level simulations through its most advanced approach, named TPM model, based on a simplification of the Volterra series following the method of separation of the low and high frequency memory effects present in PA. The model, relying on frequency domain CW characterization of the PA, shows a limitation when applied to high-power radar applications, for which this article investigates an alternate solution based on time-domain pulsed RF characterization.

关键词： behavioral modeling computer-aided design power amplifiers

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behavioral modeling of SiC MOSFET Static and Dynamic Characteristics Based on Particle Swarm Optimization Algorithm

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IEEE ACCESS 2025年 13卷 66994-67005页

作者： Zhu, Zhibo Zhao, Yang Yan, Wei Nanjing Vocat Univ Ind Technol Coll Elect Engn Nanjing 210023 Peoples R China Nanjing Normal Univ Jiangsu Engn Key Lab Elect Equipment & Electromagn Nanjing 210042 Peoples R China

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have been widely applied in electronic equipment, owing to the rapidly switching speed and superior thermal performance. Under high-frequency switching, the parasitic parameters in MOSFET driving circuit and power circuit will cause problems such as voltage overshoot and oscillation, which limit the application of power devices. To improve the stability and reliability of electronic circuit, considering that the operating frequency of the MOSFET reaches several hundred kHz, an accurate high-frequency model of the SiC MOSFET should be established, including static and dynamic models. This study proposes a SiC MOSFET behavioral model with parasitic parameters based on the particle swarm optimization (PSO) algorithm. The PSO algorithm is used to extract the parameters of the improved Enz-Krummnacher-Vittoz model and a static characteristic behavioral model of the SiC MOSFET is established, avoiding a large amount of simulation or calculation time for circuit and datasheet-drive models, the efficiency is increased by 60%, and the model accuracy is 98% compared to the datasheet. On the basis of the static characteristics, a nonlinear capacitance model is established using the PSO algorithm, and the parasitic inductance of the MOSFET is extracted through a finite element analysis. The fourth-order Runge-Kutta method is employed to solve the state equation, and the dynamic characteristic behavioral model of the MOSFET is established based on the static characteristics, parasitic capacitance and inductance, device and circuit parameters, which is simpler and easier to implement than the physical modeling method. A pulse testing experimental circuit is constructed to validate the accuracy of the dynamic model, compared with the sample model and experimental results, the errors of the behavioral model are less than 3%. This study provides valuable insights for MOSFET modeling and optimization,

关键词： Semiconductor device modeling MOSFET Integrated circuit modeling Silicon carbide Mathematical models Analytical models Accuracy Heuristic algorithms Logic gates SPICE behavioral modeling dynamic characteristic nonlinear capacitance particle swarm optimization algorithm SiC MOSFET static characteristic

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A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and behavioral modeling

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第2期34卷

作者： He, Dingzhou Southwest Univ Polit Sci & Law Coll Natl Secur Chongqing Peoples R China

The monitoring and early warning of financial risks have become a crucial link in maintaining market stability and safeguarding the rights and interests of investors. Traditional financial risk monitoring methods often rely on a single data source or analysis model, making it challenging to comprehensively and accurately capture risk signals. Therefore, this paper proposes a novel financial risk monitoring model based on multimodal neural networks, which innovatively integrates multiple data sources, such as vision, language and audio, and utilizes their inherent correlations to enhance the accuracy of risk identification. First, by employing the Bidirectional Long Short-Term Memory Network (BiLSTM) structure and incorporating the self-attention mechanism, the semantic information of financial texts is deeply analyzed through the calculation of dynamic weight coefficients. Additionally, Option-based Hierarchical Reinforcement Learning (OHRL) is utilized to accurately model the behavior of market participants, capturing nuanced changes in their decision-making process. By integrating these two types of information, a comprehensive BiLSTM-OHRL model is formulated to evaluate the risk status of financial markets in a more comprehensive and accurate manner. The results demonstrate that the model performs impressively in financial risk monitoring, accurately capturing the emotional and behavioral characteristics of market participants, thereby enhancing the comprehensiveness and predictive capability of the monitoring model. It provides robust technical support for the stable operation of the financial market.

关键词： Multimodal deep learning fraud detection sentiment analysis behavioral modeling

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Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-101 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techniques have been developed to address this issue, with surrogate modeling methods garnering particular attention due to their potential advantages. One key benefit is the promise of unprecedented acceleration in handling design problems that require repetitive system evaluations. However, behavioral modeling of antennas is an intrinsic endeavor. Challenges include the curse of dimensionality and the high nonlinearity of antenna characteristics. Moreover, design utility necessitates that the models are defined across wide ranges of frequency, geometry dimensions, and material parameters, posing a significant bottleneck for existing modeling frameworks. This paper introduces an innovative approach to constructing design-ready behavioral surrogates for antenna structures. Our methodology involves a rapid global sensitivity analysis (GSA) algorithm developed to determine a set of parameter space directions that maximize antenna response variability. The latter are obtained from spectral analysis of the GSA-based sensitivity indicators, and employed to define a reduced-dimensionality domain of the metamodel. The dependability of the model constructed in such a domain is superior over conventional surrogates while being suitable for design purposes. These benefits have been conclusively showcased using several microstrip antennas and illustrated by a number of design scenarios involving antenna geometry optimization for a variety of performance specifications.

关键词： Antenna design behavioral modeling Global sensitivity analysis Spectral analysis Dimensionality reduction EM-driven design

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Enhancing Receiver Linearity Through behavioral modeling of Measured Data and Digital Postcompensation Techniques

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Yu, Yonglin Xu, Shuo Li, Qinzheng Song, Hongqiang Su, Yanrui Yan, Fabao Shandong Univ Inst Space Sci Lab Electron Agnet Detect LEAD Weihai 264209 Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shandong Univ Shandong Key Lab Intelligent Elect Packaging Testi Weihai 264209 Peoples R China

The challenge of receiver nonlinearity in radio observation systems results in diminished system linearity, dynamic range, and sensitivity, particularly in low-amplitude and linear regions. To address these issues, this article proposes a digital postcompensation calibration method based on a nonlinear model of radio receivers. By using nonlinear digital postcompensation technology, we analyze the nonlinear effects exhibited by radio receivers. A nonlinear model of radio receivers is constructed using empirical data. The compensation-linearized output is generated by compensating for the observed input of the radio observation system's output response through the inverse model of the nonlinear model. Subsequently, the receiver response compensates for the difference between the actual radio receiver response and the response corresponding to linear gain, thereby eliminating nonlinear distortion and achieving real-time digital postcompensation linearization of these effects. We introduce a method for modeling the nonlinear behavior of radio receivers based on empirical data, along with a nonlinear postcompensation linearization approach rooted in this model. Experimental validation via radio observation systems reveals a mean square error of approximately 0.8226% between the model output and the system response. Furthermore, the nonlinear postcompensation linearization method, which is based on the nonlinear model of radio receivers, significantly enhances system linearity, particularly in the low-amplitude region and nonlinear region. The amplitude response difference of the system, concerning the quiet sun and cold space, shifts from approximately 5 dB to approximately 11 dB, thereby amplifying the sensitivity of the solar radio observation system.

关键词： Receivers Computational modeling Mathematical models Data models Linearity Extraterrestrial measurements Linearization techniques Dynamic range Calibration Training behavioral modeling linearization techniques motion compensation nonlinear systems radio receivers

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Sparsely Shared Module-Based Parameter Extraction for behavioral modeling and Digital Predistortion of RF Power Amplifiers

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年

作者： Yang, Guichen Han, Renlong Zhang, Lei Chen, Ming Qi, Ping Liu, Falin Tongling Univ Sch Math & Comp Sci Tongling 244061 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China

The extraction of linear parameters from high-dimensional features is usually time-consuming, which hinders the timely response of the digital predistortion (DPD) module to changes in the nonlinear characteristics of the radio frequency (RF) power amplifier (PA). To address this issue, we propose a novel sparsely shared module (SSM)-based parameter identification for DPD of RF PAs. Initially, we demonstrate the utilization of the shared module to integrate the commonality of PA nonlinear behaviors across different transmission configurations and to reduce the consumption of storage resources by introducing sparse model masks. This is a move that also facilitates feature mapping among heterogeneous DPD models because it avoids several redundant constructions of basis function matrices. Then, based on the sparsity of the columns in the shared module, a novel sparsity-ordering-based dimensionality reduction technique is proposed to further decrease the number of parameters to be extracted. Finally, considering the distinct performance requirements for different configurations, the sparsity of different DPD models in the shared module is flexible by adjusting allowable performance loss, which is beneficial for the on-demand allocation of the system resource. Experimental results indicate that the proposed method can attain satisfactory modeling accuracy by extracting only a few model coefficients.

关键词： Adaptation models Parameter estimation Computational modeling Feature extraction Parameter extraction Training Dimensionality reduction Radio frequency Predistortion Computational complexity behavioral modeling digital predistortion (DPD) feature mapping parameter identification power amplifier (PA) shared module transfer learning

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behavioral modeling for analog system-level simulation by wavelet collocation method

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2003年第6期50卷 299-314页

作者： Li, X Zeng, X Zhou, D Ling, XT Cai, W Fudan Univ Microelect Dept ASIC & Syst State Key Lab Shanghai 200433 Peoples R China Univ Texas Dept Elect & Comp Engn Richardson TX 75083 USA Univ N Carolina Dept Math Charlotte NC 28233 USA

In this paper, we propose a Wavelet collocation method with nonlinear companding to generate behavioral models for analog circuits at the system level. During the overall process of circuit modeling, nonlinear function approximation is an important issue to accurately capture the nonideal input-output relations of analog circuit blocks. While a great number of previous research works focus on the high-dimensional top-down design/synthesis model, which involves large analog design spaces, this paper primarily concentrates on the bottom-up verification model requiring both simple representation and high accuracy. Taking advantage of the local support of wavelet bases, a nonlinear companding method is developed to control the modeling error distribution based on system-level simulation requirements. It, in turn, significantly improves the simulation efficiency at the system level. To demonstrate the promising features of the proposed method, two circuit examples, a fourth-order switched-current filter and a voltage-controlled oscillator, are employed to build the behavioral models.

关键词： analog circuits behavioral modeling nonlinear companding wavelet collocation method

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behavioral modeling methods for switched-capacitor ΣΔ modulators

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2007年第6期54卷 1236-1244页

作者： Suarez, George Jimenez, Manuel Fernandez, Felix O. NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA Texas A&M Univ College Stn TX 77843 USA

Sigma-delta Modulators (Sigma AMs) are cornerstone elements in oversampled analog-to-digital converters and digital-to-analog converters (DACs). Although transistor-level simulation is the most accurate approach known for these components, this method becomes impractical for complex systems due to its long computational time requirements. behavioral modeling has become a viable solution to this problem. In this paper, we study styles and issues in the accurate modeling of low-power, high-speed Sigma AMs and introduce two new behavioral models for switched-capacitor (SC) integrators. The first model is based on the SC integrator transient response, including the effects of the amplifier transconductance, output conductance, and the dynamic capacitive loading effect on the settling time. The second model is based on a symbolic node admittance matrix representation of the system. Nonidealities such as jitter, thermal noise, and DAC mismatch are also addressed and included in a dual-band, GSM/WCDMA, second-order, multibit Sigma AM model with individual level averaging. VHDL-AMS and MATLAB Simulink were used as modeling languages.-Both models are validated against experimental data, showing competitive results in the signal-to-noise-plus-distortion ratio. A comparative analysis between the proposed and a traditional model is presented, with emphasis on the degrading effects due to the integrator dynamics. Moreover, a general simulation speed analysis of the proposed models is addressed.

关键词： behavioral modeling switched-capacitor (SC) integrator sigma-delta modulator (Sigma Delta Ms) Simulink SDM SC integrator VHDL-AMS SDM

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behavioral modeling of a monolithically integrated three-axis capacitive accelerometer based on the MetalMUMPs process

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR modeling AND SIMULATION INTERNATIONAL 2012年第5期88卷 565-579页

作者： Iqbal, Abid Bazaz, Shafaat A. COMSATS Inst Informat Technol Dept Elect Engn Abbottabad Pakistan

In this paper we present behavioral modeling of a monolithically integrated three-axis capacitive accelerometer using the standard MetalMUMPs process. The behavioral modeling is done in order to verify the design with respect to structural and electrostatic performance of the designed three-axis capacitive accelerometer. The proposed accelerometer is 3.2 mm x 3.5 mm in size, designed for sensing the acceleration of 25g in three axes. The mechanical noise floor for in-plane (x and y) and out-of-plane (z) axes is 0.291, 0.316, and 2.84 mu g/root (Hz), respectively. The total sense capacitance along the x, y, and z axes is 68.5 fF, 100 fF, and 6.19 pF, respectively. Sensitivity of 2.568 fF/g, 4 fF/g, and 0.252 pF/g is obtained for in-plane (x and y) and out-of-plane (z) axes, respectively. The resonance frequency for the designed accelerometer is 800 and 2500 Hz for in-plane and out-of-plane axes, respectively. The results obtained by behavioral modeling are compared with the analytical results, which are approximately the same.

关键词： behavioral modeling CoventorWare MetalMUMPs micro electro-mechanical system monolithic three-axis accelerometer

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behavioral modeling and Predistortion of Power Amplifiers Under Sparsity Hypothesis

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2015年第2期63卷 745-753页

作者： Reina-Tosina, Javier Allegue-Martinez, Michel Crespo-Cadenas, Carlos Yu, Chao Cruces, Sergio Univ Seville Dept Signal Theory & Commun Seville 41092 Spain Univ Coll Dublin Sch Elect Elect & Commun Engn Dublin 4 Ireland

A simple and flexible technique for improving the modeling and predistortion of power amplifiers is presented. The technique, which relies on the sparsity assumption for the kernel coefficients of the full Volterra (FV) behavioral model, combines a greedy algorithm for the selection of the active coefficients, a maximum likelihood method for their estimation and an information criterion for determining the best model. The approach has been applied to the design of reduced-parameters FV-based digital predistorters for three power amplifiers driven with orthogonal frequency division multiplexing signals, following the LTE and DVB-T2 standards, and a multichannel wideband code-division multiple access signal. Results show that the proposed linearizers meet the spectral masks and error vector magnitude constraints of the referred standards and provide a reduction better than 45% in the number of parameters, compared to the FV predistorters.

关键词： behavioral modeling compressed sensing digital predistortion power amplifiers Volterra series

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