A nonlinear behavioral modeling technique, based on support vector regression (SVR), is presented in this letter. As an advanced machine-learning technique, the SVR method provides a more effective way to determine the optimal model when compared with the more traditional modeling approaches based on artificial neural network (ANN) techniques. The proposed technique can overcome the well-known overfitting issue often associated with ANNs. In this letter, the basic theory of the proposed SVR modeling method is provided, along with details on model implementation in the context of RF transistor devices. Both simulation and experimental test examples for a 10-W gallium nitride (GaN) transistor are provided, revealing that the new modeling methodology provides a more efficient and robust prediction throughout the Smith chart when compared with ANNs, with the latest results showing excellent model fidelity at both the fundamental and at the second harmonic.

A new, dynamic behavioral modeling technique, based on a time-delay support vector regression (SVR) method, is presented in this paper. As an advanced machine learning algorithm, the SVR method provides an effective option for behavioral modeling of radio frequency (RF) power amplifiers (PAs), taking into account the effects of both device nonlinearity and memory. The basic theory of the proposed modeling technique is given, along with a detailed model extraction procedure. Unlike traditional artificial neural network (ANN) techniques, which take time to determine the best configuration of the model, the SVR method can obtain the optimal model in short time, using the grid-search technique. An example of an optimal SVR model selection applied to an RF PA is also given; the performance of the selected model presents a big improvement when compared with the default SVR model. Experimental validation is performed using an LDMOS PA, a single device gallium nitride (GaN) PA, and a Doherty GaN PA, revealing that the new modeling methodology provides very efficient and extremely accurate prediction. Compared with traditional Volterra models, canonical piecewise linear models, and ANN-based models, the proposed SVR model gives improved performance with reasonable complexity. In addition, it is shown that the model can predict accurately the behavior of the PA under input power levels that are different from those under which it is extracted.

The future intelligent communication systems will dynamically adjust the transmitted signal according to the radio environment and human behavior, which will lead to the rapid change of the characteristics of power amplifier (PA) and bring new challenges for digital predistortion (DPD). In this letter, a novel self-sensing DPD (SS-DPD) technique is proposed to linearize PA driven by fast time-varied signals. By automatically sensing the features of input signal and integrating them into the neural network, the proposed model is capable of linearizing the PA operated in such time-varied scenarios without updating DPD coefficients. Furthermore, the polynomial basis functions are embedded into neural network to reduce the complexity. Experimental results on a Doherty PA driven by the fast time-varied signal show that the proposed method can achieve good performance constantly with low complexity.

A new, frequency-domain, behavioral modeling methodology for gallium nitride (GaN) high-electron-mobility transistors (HEMTs), based on the Bayesian inference theory, is presented in this paper. Several different probability distribution (kernel) functions are examined for the Bayesian-based modeling architecture, with the optimal kernel function identified through experimental testing. These results are compared to an alternative approach based on the artificial neural networks (ANNs), with the data showing that the proposed approach demonstrates improved accuracy, while at the same time, alleviating the well-known ANN overfitting issue. Model verification is performed at the fundamental and harmonic frequencies using the identified optimal kernel, through comparisons with simulated data from a reference nonlinear circuit model, and with experimental data from separate 2- and 10-W GaN HEMT devices, over a wide range of load conditions. The models can predict accurately the optimal area of the fundamental output power on the Smith chart and the area of optimal power efficiency. Furthermore, the ability of the model to interpolate across input power levels and input frequencies is also tested, with excellent fidelity to the simulated and measured data obtained.

This article proposes a design methodology of dual-band outphasing power amplifier (OPA) based on two noncommensurate transmission lines (NCTLs). The devised NCTLs are used to replace the imperative reactance compensation and impedance matching networks in a conventional design. In virtue of this, the overall complexity of the combiner can be greatly reduced without sacrificing the load modulation effect. To support operations at two distinct frequencies, the conceptual NCTLs are further realized using two dual-band stub-loaded transmission lines. Comprehensive theoretical analysis is given to describe the proposed scheme and to illustrate how the electrical parameters of essential elements are determined. As a proof of concept, an OPA prototype operating at 2.6 and 3.5 GHz is fabricated. The implemented PA based on two 10-W GaN high electron mobility transistor (HEMTs) achieves 44.8 and 44.2-dBm maximum output power, and 74.5% and 62.1% peak drain efficiencies under continuous wave excitation at these two independent frequencies. At 6-dB output back-off points, the corresponding efficiencies reach 63.8% and 54.9%, respectively.

In this letter, a new Bayesian optimization (BO) method with dynamic feasible region shrinkage (DFRS) technique for power amplifier (PA) design is proposed. As a powerful optimization tool, it provides a more effective way to optimize the performance of PA than the embedded commercial optimization tools. It also has a better convergence speed than the existing fixed mode acquisition function. Results show that the new technique provides a great optimization for PA design, not only for circuit optimization but also for electromagnetic (EM) optimization.