Predistortion Of Power Amplifiers Research Articles

A Residual-Fitting Modeling Method for Digital Predistortion of Broadband Power Amplifiers

This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residual-fitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR).

Low-Complexity Digital Predistortion of Concurrent Multiband RF Power Amplifiers

The complexity of digital predistortion (DPD) for the concurrent multiband (MB) power amplifier (PA) has been a bottleneck for 5G wireless communication. A low-complexity MB DPD model has been proposed to reduce the running time complexity of DPD models for concurrent MB scenarios. The proposed MB model can generate the Volterra format basis functions with odd orders, including in-band, cross-band, and intermodulation (IMD) basis terms for arbitrary carrier configurations with an arbitrary number of bands. The optimal basis functions within the band of interests will be selected using a basis-function search algorithm. The lower cost complexity and high model accuracy of the proposed MB model have been validated by the PA linearization experiments in comparison with the memory polynomial MB model.

Digital Predistortion of RF Power Amplifiers With Phase-Gated Recurrent Neural Networks

In this article, we present a novel recurrent neural network (RNN)-based behavioral model to linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on the lightweight Just Another NETwork (JANET) unit, we propose a new neural network structure that is especially suitable for modeling the complex behavior of the RF PAs. A novel signal preprocessing technique is developed to model the complex interaction between amplitude information and phase information in the digital predistortion (DPD) model. By integrating the preprocessing stage into the RNN model, the complete phase-gated JANET (PG-JANET) model can provide enhanced linearization performance with lower complexity compared to the existing models.

Behavioral modeling and digital predistortion of RF power amplifiers based on time-delay kernel ridge regression

This paper, investigates and presents the behavioral modeling and digital pre-distortion (DPD) of radio frequency power amplifiers (RFPAs) using a time-delay kernel ridge regression (KRR) algorithm. The KRR is an advanced machine learning algorithm that can be effectively used for modeling the baseband characteristics of the RFPA considering both the effects of memory and transistor non-linearity. Compared to the traditional artificial neural network (ANN) based approach which is computationally intensive, the proposed approach using KRR with radial basis kernel function and min-max normalization method, extracts the PA behavioral and DPD model in a short time and yield consistently better results. The performance of the proposed approach in extracting PA and DPD model is demonstrated experimentally on a GaN based class AB power amplifier. The experimental results illustrates that, when compared to ANN based model, the proposed approach yields more accurate PA and DPD models, with an improvement in modeling performance by 2 dB in terms of normalized mean square error (NMSE). In addition, compared to the ANN based approach, the DPD model developed using the proposed approach exhibit improved linearization performance in suppressing spectral regrowth due to PA non-linearity.

A Low Complexity Moving Average Nested GMP Model for Digital Predistortion of Broadband Power Amplifiers

In this paper, a low complexity moving average nested generalized memory polynomial model (MAN-GMP) is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs). As the signal bandwidth increases drastically, the strong nonlinear distortions, especially those induced by the memory effect, are generated from the highly efficient PAs. To compensate for the strong memory effect, a moving average nested envelope memory polynomial (MAN-EMP) model is derived from an accuracy-enhanced GMP model, which offers reduced complexity while suffering from degraded modeling accuracy. The MAN-GMP model is further proposed to improve the modeling accuracy by connecting several memory branches of the MAN-EMP model in parallel. An iterative algorithm is designed to extract the model coefficients efficiently through only one or two iterations. Experimental measurements are carried out on two sub-7 GHz broadband GaN Doherty PAs with up to 200 MHz bandwidth OFDM signals to benchmark the proposed MAN-GMP model against the GMP, the parallel-LUT-MP-EMP (PLUME), the augmented complexity-reduced GMP (ACR-GMP), the generalized twin-nonlinear two-box (GTNTB), and the enhanced Wiener models. The experimental results show that the MAN-GMP model can effectively compensate for the nonlinear distortion of broadband PAs with significant complexity reduction.

Self-Sensing Digital Predistortion of RF Power Amplifiers for 6G Intelligent Radio

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.

Block-Oriented Time-Delay Neural Network Behavioral Model for Digital Predistortion of RF Power Amplifiers

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.

Complexity Optimized Digital Predistortion Model of RF Power Amplifiers

A novel behavioral modeling technique for digital predistortion of radio frequency power amplifiers (PAs) is proposed. The proposed basis-propagating selection (BAPS) model has an advantage on complexity since its basis functions are constructed as delay or a combination of existing basis functions. The BAPS model effectively optimizes the running time complexity while maintaining the generality of a full Volterra series. A greedy search procedure is applied to select the optimal basis functions. Compared with truncated Volterra models and pruned models obtained by popular pruning techniques, the superior performance of the BAPS model, in terms of accuracy and identification stability, has been fairly justified. The linearization measurements on different types of PAs illustrate that the BAPS model can robustly outperform other models from literature while achieving a low running complexity.

Reducing Power Consumption of Digital Predistortion for RF Power Amplifiers Using Real-Time Model Switching

In this article, we propose a new behavioral modeling method to reduce the running complexity and power consumption of digital predistortion (DPD) models for radio frequency power amplifiers. By employing the proposed method, different cross terms in a DPD model can be switched dynamically in real time. Each cross-term branch can further choose the model coefficients from multiple coefficient sets, which improves the DPD performance with little extra complexity. The switch of both cross-term branches and model coefficients is realized using a decision tree-based switch controller, leading to very low run-time complexity. By optimizing the selection process, different input samples can choose the most suitable model configuration that contributes most to the linearization performance. As only one branch is activated at a time, the power consumption can be greatly reduced. Based on the experimental results, the proposed method can achieve excellent linearization performance with significantly reduced power consumption.

From MHz to THz: Systems and Applications

This Special Issue is formed by collecting 46 regular papers on the topic of systems and applications, which reflects the latest progress in microwave systems and applications from MHz to THz. All those 46 papers are divided into six subtopics, namely, digital predistortion of RF power amplifiers (five articles), radar systems and applications (14 articles), wireless systems (nine articles), detectors and imaging (ten articles), microwave applications (five articles), and microwave power transfer technologies (three articles).

A Digital Predistortion for Concurrent Dual-Band Power Amplifier Linearization Based on Periodically Nonuniform Sampling Theory

In this article, we propose a novel technique of digital predistortion (DPD) for dual-band power amplifiers (PAs) based on the periodically nonuniform sampling (PNS) theory. In contrast to conventional dual-band DPD (2-D-DPD) models, the proposed periodically nonuniform sampled DPD (PNS-DPD) has only a single input, which can largely reduce the model complexity. We fold the two stimuli with aliasing and feed them to a simple single-band DPD model. The desired predistorted signals are reconstructed from aliased DPD output through the PNS theory. Compared with conventional multi-input models that include numerous intermodulation products of the input signals, the complexity of the proposed single-input PNS-DPD model is hugely decreased. The model coefficients of the proposed PNS-DPD can be easily extracted with conventional direct or indirect learning architecture. We experimentally evaluate the proposed DPD on a test bench and compare it with other DPD techniques in the literature. The implementation complexity can be reduced by over 30%, and the identification complexity is also largely reduced.

A Band-Limited Magnitude-Selective Affine Function-Based Model for Digital Predistortion of 5G Broadband Power Amplifiers

A band-limited magnitude-selective affine (MSA) function-based model is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs) in 5G millimeter-wave frequency band. The new model is derived from the band-limited canonical piecewise linear function-based model by employing MSA functions, which can obviously reduce the number of multiplication operations and the model complexity. Experimental results show that the proposed model gives better normalized mean square error and DPD performance in comparison with some existing models when a broadband PA is excited by a 5G new radio (NR) signal of 400-MHz bandwidth at 28-GHz band.

A Low Sampling Rate Memory-Grouped Method for Digital Predistortion With Constrained Acquisition Bandwidth

A novel memory-grouped method is proposed for digital predistortion (DPD) of power amplifiers (PAs) with constrained acquisition bandwidth (BW) and extremely low sampling rate. By grouping the basis functions with different memory delays, a two-step algorithm can reduce overfitting in the constrained-BW technique. As a result, excellent DPD performance can be achieved even when the acquisition BW is constrained to several times narrower than the BW of the original input signal. Thus, using the memory-grouped method, a very low-speed analog-to-digital converter (ADC) can be used to sample the constrained feedback signal without aliasing, significantly reducing the cost of configuring a high-speed ADC. The performance of the proposed method is compared with the existing methods in different scenarios. Experimental results show that when the feedback BW is less than the BW of the original input signal, the proposed method can still achieve good performance, while the traditional methods fail to work. The experiment results also imply that the memory-grouped method can reduce the sampling rate by more than ten times.

Digital Predistortion of Nonlinear Power Amplifier Based on Improved Spline Adaptive Filtering Method

To improve the modeling accuracy and digital predistortion compensation effect, this paper proposed a kind of polynomial based spline adaptive filter (SAF) power amplifier (PA) behavioral modeling method. The proposed model can solve the shortcomings of the traditional memory polynomial model (MP), such as higher nonlinear order, highly coefficient calculation difficulty, poor hardware stability, low modeling accuracy and poor digital predistortion compensation. Meanwhile, the proposed SAF based model can avoid the pseudo-inversion operation of the matrix when calculating the coefficients through the indirect learning structure, which can improve the compensation effect of digital predistortion. In order to compare the performance of the different models, the modeling processes of original filtering model, MP model and dynamic variation reduction (Dynamic Deviation Reduction, DDR) model based on SAF method (OSAF, MPSAF, DDRSAF) are presented respectively. The proposed models are verified by a class F PA, which are stimulated by a set of measured input and output data of an LTE signal with 20 MHz bandwidth. The measured results shows that compared with the conventional MP model, the proposed DDRSAF model presents the best model performance and improves the modeling accuracy by 6.6dB while maintaining the similar computational complexity, and the ACPR compensation effect increases by 19.58dB and 20.47dB respectively, which have the important significance for practical engineering application.

Sparse Flexible Reduced-Volterra Model for Power Amplifier Digital Pre-Distortion

The Volterra framework plays an important role in digital pre-distortion (DPD) for power amplifier (PA) linearization. In practice, the full-Volterra (FV) model is avoided due to the so-called curse of dimensionality, which has motivated several Volterra pruning techniques in the literature. However, choosing the pruned-Volterra structure with a given complexity that most accurately describes a PA or DPD with unknown characteristics often requires trial and error. The motivation of this article is to propose a reduced-Volterra model that extends traditional memory polynomials, e.g., the generalized memory polynomial (GMP), by including higher-dimensional terms in a flexible and controlled way, avoiding the curse of dimensionality of the Volterra model and resulting in a model that can apply to a wide range of PA and DPD. Furthermore, the article investigates the parsimonious sizing of the proposed model using least absolute shrinkage and selection operator (LASSO) and sparse-group LASSO (SGL) convex optimization, significantly reducing the model’s running cost, while preserving its performance. In our experimental validation, the sparse versions of the proposed model achieved 3 dB better adjacent channel power ratio (ACPR) than the conventional GMP model for the same or lower DPD running cost, at the expense of a more costly estimation, which is performed off-line.

Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

In this article, we propose a new behavioral modeling approach, called boosted model tree, to characterize and compensate for the complex nonlinear distortions induced by wideband high-efficiency radio frequency power amplifiers. With the proposed model, the input data are classified into different zones by decision trees and each zone is assigned separate submodels. We also employ a model boosting technique to build multiple parallel tree structures that jointly model the desired nonlinear behavior. By designing dedicated optimization procedures, both tree structures and submodel coefficients can be efficiently identified. It is demonstrated that the combination of piecewise and parallel structures provides a powerful and hardware-efficient way to model nonlinear memory effect and cross terms. Based on the experimental results, the proposed method can achieve improved linearization performance with low hardware complexity under challenging wideband predistortion scenarios.

Signed Orthogonal Regressor Algorithm for Digital Predistortion of Power Amplifiers

Digital predistortion (DPD) of broadband power amplifiers (PAs) requires a large number of parameters to compensate for the severe nonlinear distortion, which may cause considerable computational complexity in the DPD parameter identification process. To solve this problem, we proposed a novel signed orthogonal regressor algorithm (SORA) to simplify the identification of DPD parameters. Based on the signed regressor algorithm and principal component analysis (PCA), the proposed algorithm can eliminate most of the multiplication operations in the identification process and greatly reduce the computational complexity. Furthermore, the proposed SORA is modified to be able to estimate each parameter independently. Experimental results show that compared with the conventional PCA-based method, the proposed two methods can realize comparable linearization performance with significantly lower computational complexity.

Directed Graph Navigated Digital Predistortion of mmWave Power Amplifiers for 6G Hopping Applications

To exploit the limited communication resources, the real-time carrier frequency of the power amplifier (PA) may hop, which will result in rapid change in PA’s nonlinear behavior and bring huge challenge to make the digital predistortion (DPD) response in a timely manner. Different from the conventional methods that attempt to shorten the DPD updating time, a novel scheme of controlling its nonlinear variation is proposed. By analyzing PA’s various behaviors using graph model and extracting a directed graph to navigate its power adjustments, the variation in nonlinear behavior induced by hopping carrier frequency can be effectively compensated, which will mitigate the DPD performance deterioration when there is not enough time for DPD update. Finally, the proposed method was validated by experiments on a 100-MHz modulated signal stimulated millimeter-wave (mmWave) PA operating at five hopping carrier frequencies from 26.4 to 28 GHz, with the step of 400 MHz.

Thresholds Optimization of Decomposed Vector Rotation Model for Digital Predistortion of RF Power Amplifier

Thresholds Optimization of Decomposed Vector Rotation Model for Digital Predistortion of RF Power Amplifier

HIGH POWER AMPLIFIER PREDISTORTER ASIC IN STANDARD DIGITAL CMOS TECHNOLOGY

Satellite communications offer a wide coverage for global communication systems. In order to increase spectral efficiency, non constant modulus constellations are very attractive compared to the classical BPSK and QPSK schemes. A 16-QAM modulation could offer a significant increase in spectral efficiency for satellite communications. Because the available power on board the satellite is strongly limited, High Power Amplifiers (HPA) like Travelling Wave Tubes (TWT) or Solid State Power Amplifiers (SSPA) are generally operated near the saturation point with a low back off. When operated with such low back off, HPA are highly non linear amplifiers. So, the signal to amplify being strongly distorted, a predistorter has been developed. A high power amplifier predistorter has been implemented in 0.6 m CMOS technology. First, the predistorter is briefly described. Then, the implementation of the predistorter is shown. The circuits designed for the neuron first layer are described, as well as the simulation results obtained.