Predistortion Of Power Amplifiers Research Articles

Res-GAN for Behavioral Modeling and Pre-Distortion of Power Amplifiers in OFDM-Based Satellite Communication System

Res-GAN for Behavioral Modeling and Pre-Distortion of Power Amplifiers in OFDM-Based Satellite Communication System

A Unified Neural Network-Based Approach to Nonlinear Modeling and Digital Predistortion of RF Power Amplifier

A Unified Neural Network-Based Approach to Nonlinear Modeling and Digital Predistortion of RF Power Amplifier

Iterative Multimetric Model Extraction for Digital Predistortion of RF Power Amplifiers Using Enhanced Quadratic SPSA

Iterative Multimetric Model Extraction for Digital Predistortion of RF Power Amplifiers Using Enhanced Quadratic SPSA

A Block-Based LMS Using the Walsh Transform for Digital Predistortion of Power Amplifiers

A Block-Based LMS Using the Walsh Transform for Digital Predistortion of Power Amplifiers

Gated Dynamic Neural Network Model for Digital Predistortion of RF Power Amplifiers With Varying Transmission Configurations

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> with only a slight performance loss.

Multi-Output Recurrent Neural Network Behavioral Model for Digital Predistortion of RF Power Amplifiers

In this letter, we propose a method for behavioral modeling and digital predistortion (DPD) of RF power amplifiers (PAs) based on multi-output recurrent neural networks (RNNs). RNN has high modeling accuracy, but it also has high running complexity due to the recurrent mechanism. For this reason, we propose a multi-output model architecture, which means that the DPD model produces multiple adjacent outputs simultaneously for a single input sample group. This approach greatly reduces the running complexity of DPD based on RNNs with essentially no deterioration in performance. The proposed multi-output mechanism is applied to both long short-term memory (LSTM) and gate recurrent unit (GRU), and excellent linearization performances are maintained.

Pruned Basis Space Search for Digital Predistortion of RF Power Amplifiers

A novel behavioral modeling technique called pruned basis space search (PBSS) is proposed for digital predistortion (DPD) of RF power amplifiers (PAs). The PBSS finds the optimal DPD model by basis function search in the pruned basis space (PBS). The PBS is obtained by sparsifying the basis space comprising a wide variety of basis functions, while the basis function search is implemented based on heuristic algorithms. A basis function multiplexing-based complexity identification algorithm is proposed to improve the fitness calculation so that the basis function search can balance the performance and running complexity of the behavioral model. The PBSS model avoids the shortcomings of traditional truncated models and various popular Volterra series-based behavioral modeling approaches and thus offers superior performance. The experimental part performs behavioral modeling and linearization tests on two different PAs. The experimental results confirm that the PBSS model can achieve a better tradeoff between linearization performance and complexity than the state-of-the-art Volterra series-based model.

Low Complexity Adaptive Model for Digital Predistortion of RF Power Amplifiers in Time-Varying Configurations

A novel behavioral modeling approach called adaptive model tree (AMT) is proposed for digital predistortion (DPD) of RF power amplifiers (PAs) in fixed and time-varying configurations. The AMT model is piecewise based on the decision tree and the reduced-complexity full basis-propagating selection (RC-FBPS) model. A novel two-step joint iterative algorithm is proposed to achieve a good match between the decision tree and the submodels obtained from the RC-FBPS model. The AMT model inherits and enhances the respective advantages of the decision tree and RC-FBPS model to have a powerful adaptive capability potentially. The experimental tests on a Doherty PA confirm that the AMT model can achieve a better trade-off between linearization performance and complexity than the state-of-the-art model in the fixed configuration. Furthermore, to characterize and compensate for the complex dynamic nonlinear distortions of PAs in time-varying configurations, the piecewise modeling technique in time-varying configurations is proposed and applied to the AMT model in this article. The experimental results confirm that the AMT model achieves excellent linearization performance with very low complexity in time-varying configurations and good generalization performance for new configuration combinations that are not used for training.

Digital Predistortion of RF Power Amplifier Using the NARX-SVR Model

This letter presents the digital predistortion for power amplifiers (PAs) using a new behavioral model that combines a nonlinear autoregressive exogenous architecture with a support vector regression (NARX-SVR) algorithm. Unlike other models based on SVR, the NARX-SVR requires lower support vector numbers (SVN) and floating-point operations (FLOPs) without sacrificing the accuracy of the dynamic nonlinear behavior model of the PA. The NARX-SVR model was validated by modeling and predistorting a commercial GaN hybrid Doherty PA (HDPA) driven with a 2- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 20$ </tex-math></inline-formula> -MHz LTE signal with an output power of 42 dBm. Experimental results show excellent agreement between measured and modeled AM–AM and AM–PM data. The distortion correction is better than 14 dB, and the error vector magnitude (EVM) is less than 2%. Besides, the FLOPs and SVN are reduced by 18% and 20%, respectively, compared with the dynamic time-delay SVR model.

A low complexity loop‐delay estimation algorithm based on sliding window for power amplifier characterization

AbstractIt is well known that accurate estimation of the loop delay between the input and output feedback signals is the key to behavioral modeling and digital predistortion of power amplifier. Nevertheless, most of the traditional loop‐delay compensation algorithms have the disadvantage of high computational complexity. For the purpose of solving this problem, a loop‐delay estimation algorithm based on sliding window has been proposed in this paper, where the first derivative characteristic of the signal is employed to evaluate the correlation between the input and output feedback signals. The simulation and experiment are carried out with different bandwidth signals in this paper. The results show that the proposed algorithm and the traditional cross‐correlation‐based method could align the input and output feedback signals successfully. But in terms of resource consumption, the proposed algorithm could reduce the multiplier nearly by 100% and the adder by approximately 93.2%.

Block-Oriented Recurrent Neural Network for Digital Predistortion of RF Power Amplifiers

Block-Oriented Recurrent Neural Network for Digital Predistortion of RF Power Amplifiers

A Low Running Complexity Model for Digital Predistortion of RF Power Amplifiers

In this letter, two basis function multiplexing-based behavioral modeling methods for digital predistortion (DPD) of RF power amplifiers (PAs) are proposed to reduce the running complexity of DPD. The proposed full basis-propagating selection (FBPS) model and reduced-complexity FBPS (RC-FBPS) model give two reasonable ways to multiplex even-order basis functions, extending the basis-propagating selection (BAPS) model which only uses basis function delay and odd-order basis functions. The experimental results confirm that both the proposed FBPS and RC-FBPS models can achieve a good tradeoff between running complexity and performance.

Segmented Spline Curve Neural Network for Low Latency Digital Predistortion of RF Power Amplifiers

At present, we are the dawn of a new era for wireless communication systems. The new dynamic approach to the operation of cellular networks requires an extension of the essential input–output hardware mechanisms, to include intelligence. Traditional neural network structures can be used to embed artificial intelligence into cellular network base stations; however, standard network structures are not an optimal solution for these use cases. In this article, we present a neural network structure, which is specifically designed to provide a more accurate and computationally efficient solution compared with the previous neural network solutions for predistortion of RF power amplifiers (PAs). The proposed network structure is directly compared with alternative neural network solutions, which have been successfully employed for digital predistortion (DPD). The operation of this network is validated for DPD with experimental measurements with wideband signals using the latest generation of commercially available RF hardware. The novel network structure proposed in this work is demonstrated, in practice, to have better performance for a normalized mean square error (NMSE), an adjacent channel leakage ratio (ACLR), and an error vector magnitude (EVM) compared with the most popular previously published neural network.

Continual Learning Digital Predistortion of RF Power Amplifier for 6G AI-Empowered Wireless Communication

Artificial intelligence (AI) provides opportunities to enable high-efficiency wireless communication to dynamically adapt to the local environments and user demands. In this article, a continual learning digital predistortion (DPD) algorithm is proposed to linearize radio frequency (RF) power amplifier (PA) in 6G AI-empowered wireless communication. It is achieved by employing the ability of knowledge retention and knowledge transfer to merge the new PA operating state into the previous model, which makes the model more intelligent when dealing with multiple states. In addition, an augmented self-sensing model is proposed to accurately capture the common characteristics and key dynamic characteristics among different PA operating states to improve the knowledge transfer ability during the continual learning process. The experiments were carried out on a Doherty PA with dynamic configurations associated with power, bandwidth, and signal type. The experimental results have shown that the proposed technique can continuously learn when faced with unpredicted new PA operating states and so as to linearize all known states. The proposed DPD method performs with very low complexity in the long term, which is very suitable for the emerging AI-empowered dynamic scenario.

Digital Predistortion of RF Power Amplifiers With Decomposed Vector Rotation-Based Recurrent Neural Networks

In this article, we present a novel decomposed vector rotation (DVR)-based recurrent neural network behavioral model for digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs) in wideband scenarios. By representing memory terms of DVR with recurrent states and redesigning the piecewise modeling, we propose a novel recurrent DVR scheme. To ensure stable operation and enhanced modeling accuracy, we integrate the recurrent DVR into the gated learning mechanism of the modified Just Another NETwork (JANET). Experimental results confirm that the proposed DVR-JANET model provides much improved linearization performance with significantly reduced model complexity compared with the recent existing models.

A pruning method of feedforward neural network based on contribution of input components for digital predistortion of power amplifier

AbstractIn this paper, a pruning method based on contribution of input components is proposed to optimize the structure of Feedforward neural network (FNN) model for digital predistortion of power amplifier. The optimized hyperparameters are the specific input components. The pruning algorithm is divided into two stages. The stage I is used to rough rank the importance of input components and the stage II is used to modify the results of stage I. Then, we can get an importance ranking of input components. By deleting unimportant input components, a pruned FNN model with fewer input components can be obtained. Test results show that the proposed algorithm can greatly simplify the FNN model on the premise of ensuring the predistortion performance.

Residual RNN Models With Pruning for Digital Predistortion of RF Power Amplifiers

The paper presents several novel residual recurrent neural network (RNN) models for digital predistortion of radio-frequency power amplifiers. Then, unstructured and structured neural network (NN) pruning algorithms are proposed to reduce the per-sample computational complexity and the required memory, without compromising their modeling performance. The mathematical equations of the complexity and the memory are derived to analyze the hardware cost and design low cost models. An experimental platform is developed to measure the modeling performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error, error vector magnitude (EVM) and bit error rate. The platform uses an envelope tracking PA excited by 80 MHz IEEE 802.11ac signals. Compared with the state-of-the-art RNN model in (Sun <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019), the proposed models reduce the per-sample complexity and required memory by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$94\hbox{--}99$</tex-math></inline-formula> %, they also improve the ACLR and EVM by at least 3 dB and 1 dB at 0.45 dB and 3.15 dB gain compression, respectively. Furthermore, an observation path with time alignment is used to remove the transmit latency in the state-of-the-art approach. At 3.15 dB gain compression, the proposed models outperform the state-of-the-art feedforward and convolutional NN models by at least 1.3 dB in EVM, outperform the general memory polynomial model by at least 3.3 dB in EVM. The above evidences indicate that the proposed RNN models can be implemented with relatively low hardware cost and achieve the best EVM in high power efficiency scenario.

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.