This paper presents two novel neural network models for radio-frequency (RF) power amplifiers (PAs): vector decomposed time-delay neural network (VDTDNN) model and augmented vector decomposed time-delay neural network (AVDTDNN) model. In contrast to conventional neural network-based models, VDTDNN and AVDTDNN comply with the physical characteristics of RF PAs by employing carefully designed network structures. In particular, the nonlinear operations are conducted only on the magnitude of the input signals, while the phase information is recovered with the linear weighting. Linear terms with shortcut connection, as well as high-order terms, can be used to further boost the modeling performance. The complexity analysis shows that the proposed models have significantly lower complexity than the existing neural network models. A wideband GaN RF PA excited by the 40- and 60-MHz OFDM signals were employed to evaluate the performance. The extensive experimental results reveal that the proposed VDTDNN and AVDTDNN models can achieve better linearization performance with lower computational complexity compared with the existing neural network-based models.

The method of moments (MOM) is used in conjugation with the compressive sensing (CS) to efficiently analyze the electromagnetic scattering problems over a wide incident angle. A new incident source is formed based on CS theory, in which much information from different incident angle is included. By solving the integral equations under this new incident source, one can obtain the measurements of the induced currents without calculating the problems with a finer angle increment. Finally, the orthogonal matching pursuit technique (OMP) is used to recover the real induced currents. Numerical simulations for differently shaped objects are presented to validate the effectiveness of the proposed method.

This paper proposes two novel vector decomposed neural network models for behavioral modeling and digital predistortion (DPD) of radio-frequency (RF) power amplifiers (PAs): vector decomposed long short-term memory (VDLSTM) model and simplified vector decomposed long short-term memory (SVDLSTM) model. The proposed VDLSTM model is a variant of the classic long short-term memory (LSTM) model that can model long-term memory effects. To comply with the physical mechanism of RF PAs, VDLSTM model only conducts nonlinear operations on the magnitudes of the input signals, while the phase information is recovered by linear weighting operations on the output of the LSTM cell. Furthermore, this study modifies the LSTM cell by adding phase recovery operations inside the cell and replacing the original hidden state with the output magnitudes that are recovered with phase information. With the modified LSTM cell, a low-complexity SVDLSTM model is proposed. The experiment results show that the proposed VDLSTM model can achieve better linearization performance compared with the state-of-the-art models when linearizing PAs with wideband inputs. Besides, in wideband scenarios, SVDLSTM model with much fewer parameters can present comparable linearzation performance compared to VDLSTM model.

A novel block-oriented time-delay neural network (BOTDNN) model for dynamic nonlinear modeling and digital predistortion (DPD) of RF power amplifiers (PAs) is proposed. The proposed model consists of a dynamic linear network and a static nonlinear network to characterize dynamic nonlinear systems. The dynamic linear network simulates multiple linear filters using a fully connected layer with the linear activation function. The static nonlinear network is constructed based on vector decomposition and phase recovery mechanism. To validate the proposed model, experiments have been carried out with two different PAs operating at 2.4 and 39 GHz, respectively. The test results demonstrate that the proposed model has better PA modeling and nonlinear compensation capabilities than state-of-the-art PA behavioral models, while with significantly lower model complexity. Furthermore, to reduce the system cost, we investigate the problems that arise when the neural network-based behavioral models are applied to low feedback sampling rate DPD and propose an improved method. The experiments confirm that the proposed low feedback sampling rate DPD method can effectively alleviate the deterioration of linearization performance caused by undersampling.

The future intelligent transmitter will dynamically adjust the transmission configuration on demand, which will bring new challenges to digital predistortion (DPD). In this article, we present a gated dynamic neural network (GDNN) DPD model to linearize the power amplifier (PA) with varying transmission configurations. The proposed GDNN model is composed of a gating network and a backbone network that can be any NN-based DPD model designed for a fixed configuration. The core idea of the GDNN model is that the backbone model can be dynamically adjusted using the configuration-dependent weights generated by the gating network to achieve transmission configuration-adaptive DPD. To further reduce the running complexity of the GDNN DPD, a sparse GDNN (SGDNN) DPD model is also proposed, which selectively activates the neurons of the backbone network according to the transmission configuration. Experiments are performed with a Doherty PA to validate the proposed method, where the varying transmission configuration includes power level, signal bandwidth (BW), and peak-to-average power ratio. The test results demonstrate that the proposed method can effectively linearize the PA with dynamic transmission configuration and has excellent configuration generalization capability. Moreover, the sparse gating technique can reduce the running complexity of the GDNN DPD by more than 50 $\%$ with only a slight performance loss.

A high-efficiency concurrent dual-mode dual-band power amplifier is presented. It simultaneously operates at class-F mode for one band and at inverse class-F mode for the other band. With this mode assignment method, the conflict in harmonic impedance control for concurrent dual-band applications is avoided. Experimental results show a high performance of more than 80% peaking power added efficiency with 40.6 and 41.8 dBm output power in the sampled bands of 0.8 and 1.25 GHz, respectively.