A novel, broadband, nonlinear behavioral model, based on support vector regression (SVR) is presented in this paper. The proposed model, distinct from existing SVR-based models, incorporates frequency information into its formalism, allowing the model to perform accurate prediction across a wide frequency band. The basic theory of the proposed model, along with model implementation and the model extraction procedure for radio frequency transistor devices is provided. The model is verified through comparisons with the simulation of an equivalent circuit model, as well as experimental measurements of a 10 W Gallium Nitride (GaN) transistor. It is seen that the efficiency prediction throughout the Smith chart, for varying fundamental and second harmonic loads, across a wideband frequency range, show excellent fidelity to the measured results. Device dc self-biasing is also modelled to allow prediction of power amplifier (PA) efficiency, which is shown to be highly accurate when compared with corresponding measured data. Finally, a class-J PA is constructed and measured across the frequency with a large-signal input tone. The resulting measured and modelled values of key PA performance figures are shown to be in excellent agreement, indicating the model is suitable for broadband PA design.

This paper focuses on the behavioral modeling of the passive RF blocks from frequency-domain samples. This work is based on vector fitting (VF) approach which is a robust method for MIMO system identification in the frequency domain. This paper addresses firstly the problem of convergence using envelope transient (ET) on complex circuits, i.e. power amplifiers (PAs), where linear sub-circuits are widely described through S-parameters matrices derived from ElectroMagnetic (EM) simulations. An alternative way leads to combine VF method with an RLC synthesis process at the circuit level. This approach is validated on a simple circuit case and generalized to MIMO systems. Second application is the behavioral modeling of MIMO blocks at system level in a high-level spice-like system simulation tool which allows “Control Flow” simulations. The proposed approach combines on one hand the VF method with and impulse responses evaluation on the other hand. In consequence, bilateral behavioral models of MIMO system can be efficiently achieved irrespective of the number of access. This method is validated at once on simple and complex MIMO blocks. As a result, the topological behavioral modeling of complex PAs, which distinguish linear sub-circuits and nonlinear ones, is now possible in a high-level time-domain CAD tool.

The design of agile active antennas requires an efficient modeling methodology in order to quantify the impact of other components on the array radiation pattern, and especially the influence of power amplifiers (PA). Therefore, the performance prediction of PA on TX chains is of prime importance. This article describes two different approaches for active antenna applications. The first one concentrates on PA macro-modeling, which takes into account a large output load impedance mismatch with a voltage standing wave ratio up to 4:1. A PA behavioral model based on nonlinear scattering functions was developed and extracted from CW measurements. The model validity was checked by comparison with the measured data. The second one describes a novel technique for synthesizing a given radiation pattern, whereas taking into account the mutual coupling and calculated matching impedances (ZL ≠ 50 Ω) of each antenna in the array according to frequency and pointing angle.

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both short- and long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. The NARMA-based digital predistorter is then derived and formulated in terms of basic predistortion cells, especially suitable for efficient implementation in an FPGA. The performance of the predistortion solution is preliminarily assessed by means of computer simulations.

Sustainable cropping systems use crop diversity as a foundation to reduce the environmental impacts of agrichemicals and risks of pest and disease outbreaks. Although greater diversity in the agricultural landscape is an important goal, the decisions of individual farmers determine the diversity of crops used in each farming system. Therefore, it is essential to gain an understanding of important factors in farmers' decisions as part of any proposed solution to low crop diversity. Our objective was to develop and test an integrated socioeconomic model of farmers' decisions on crop rotations as an indicator of overall crop diversity. The model incorporated measures of farmers' attitudes, net household income, control over decisions, social norms, and regional location to address the social, economic, and environmental factors of crop diversity. Mail survey responses from 197 farmers from a western Corn Belt county in 1998 supplied the data for the analysis. Location had the strongest influence on crop diversity in the model, on the basis of a standardized beta statistic of 0.46 in the ordinary least squares regression analysis. Farmers in the region with the highest degree of sloping land had significantly higher crop diversity than farmers in the most productive, relatively flat area. Lower household net income and positive attitudes toward crop rotations were also associated with higher crop diversity. We believe social norms and control would exert more influence in uncertain decisions. The results of this study suggest that, to maintain and possibly expand crop diversity in the future, targeting farmers in sloping landscapes with positive attitudes toward rotations would be the best approach to focus economic incentives for diverse crop rotations and technical support from Extension. In any effort to support crop diversity greater than a typical cornsoybean rotation, it is also critical to preserve integrated forage/crop/livestock systems. Further adaptation and application of our behavioral model in other settings would help to better evaluate, understand, and target the integrated decisions people make relative to crop diversity.