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This paper explores the benefits of utilizing multistage cascaded (CC) behavioral models for digital predistortion (DPD) linearization of wideband high-efficiency power amplifiers (PAs). To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction technique based on a greedy algorithm is proposed. The advantages of employing CC DPD models with gradient descent parameter identification, as opposed to single-stage DPD models with least squares parameter identification, are extensively demonstrated and analyzed. The trade-off among linearity, power efficiency and computational complexity is evaluated considering the linearization of a high-efficiency pseudo-Doherty load-modulated balanced amplifier (PD-LMBA). Using the proposed pruning strategy for CC DPD models, we demonstrate a significant reduction in the number of parameters needed to linearize the PD-LMBA. The PA operates at an RF frequency of 2 GHz and delivers a mean output power of 40 dBm with an approximately 50% power efficiency when driven by 5G new radio signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio.

This paper proposes a Composition Piecewise Memory Polynomial (CPMP) digital predistortion model based on a Vector Switched (VS) behavioral model to address the challenges of severe nonlinearity and strong memory effects in wideband power amplifiers (PAs). To tackle this issue, two thresholds are calculated and used to segment the envelope values of the input signal according to the nonlinear distortion characteristics of the PA. In this approach, a Generalized Memory Polynomial (GMP) model is employed for the lower segment, a Memory Polynomial (MP) model is employed for the middle segment, and a higher-order GMP model is employed for the upper segment. By sharing the fundamental MP among the proposed segmented models and leveraging a design methodology that configures different cross terms, memory depths, and polynomial orders for each segment, this model achieves superior linearization performance while simultaneously reducing the computational complexity associated with model extraction. The experimental results demonstrate that the adjacent channel power ratio (ACPR) of the predistorted PA output signal using the proposed model improves from −36 dBc to −54 dBc, matching the performance of the GMP model. Furthermore, this performance is 0.5 dBc better than the Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models. Notably, the complexity of the proposed parameter extraction process is 28.8% of the DVR model, 21.79% of the GMP model, and 12.83% of the PDDR model. [ABSTRACT FROM AUTHOR]

In this paper, a sparse smooth twin support vector regression (Sparse‐STSVR) model for power amplifier (PA) behavioral modeling is obtained by pruning the kernel matrix based on Cholesky decomposition. Based on the primal smooth twin support vector regression (STSVR) model, the Nystrom approximate matrix of the kernel matrix is found to replace the original kernel matrix, thus simplifying the Newton iterative parameter extraction process of the primal STSVR model and accelerating the convergence of the algorithm. In addition, the new rank approximation kernel matrix has the characteristic of sparse parameters, which further reduces the computational complexity of the feedforward link of the digital predistorter. The 100 MHz 5G New Radio (NR) signal is used for verify the effect of PA modeling and digital predistortion (DPD) experiment. The results show that the proposed method can improve the normalized mean square error (NMSE) by about 2 ~ 3 dB with fewer coefficients compared with the previously proposed machine learning model, and the predistortion linearization effect improves by nearly 3 dB on the adjacent channel power ratio (ACPR), which achieves a good trade‐off between model performance and computational complexity. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The evolution of Digital Predistortion (DPD) for Power Amplifier (PA) linearization in wireless communications has seen a natural progression towards increasingly complex DPD models. This complexity directly correlates with improved accuracy in modeling PAs, thereby enhancing the linearization performance of DPD systems. As the demand for bandwidth and PA operation frequencies continues to rise, there is a growing trend towards achieving higher accuracy at the expense of increased complexity. This trend poses challenges for DPD design, necessitating the introduction of more complex algorithms to ensure effective PA linearization. This phenomenon is especially apparent in Volterra Series DPD algorithms, which are widely used for modelling nonlinear systems like PAs. This paper showcases the steps of reducing the modelling operations complexity of the Simplified Volterra (SV) method. The treated SV method presented in this paper is compared against the original SV method through simulation. The DPD model operations complexity is evaluated using the number of required multiplication operations. The DPD PA linearization performance is evaluated with respective model’s Normalized Mean Square Error (NMSE). A 57% reduction on the number of multiplication operations is observed in the treated SV against the original SV. Measured NMSE is similar for both models, indicating the treated SV model achieves this reduction in complexity without sacrificing linearization performance. [ABSTRACT FROM AUTHOR]

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path. [ABSTRACT FROM AUTHOR]

In the fifth-generation wireless communication system (5G), RF power amplifiers (PAs) exhibit severe non-linear distortion and pronounced memory effects, particularly when employed in complex high-order modulation schemes such as 16QAM and 64QAM. To investigate and enhance the linearization capability of wideband RF PAs, a wideband RF PAs design based on the advanced design system (ADS) is proposed. Operating within a central frequency of 3.55 GHz, the memory polynomial (MP) model is employed to construct a digital pre-distorter, achieving a normalized mean square error (NMSE) metric of -33.28 dB. Finally, simulation-based verification is conducted using 5G new radio (NR) signals employing 16QAM or 64QAM high-order modulation with a bandwidth of 100 MHz and a signal peak amplitude of 9.899 dB, centered at a frequency of 3.55 GHz RF PAs. The simulation results demonstrate that under 16QAM modulation, the adjacent channel power ratio (ACPR) can be improved by 20.13 dB and 20.05 dB, respectively. For 64QAM modulation, ACPR improvement rates of 18.35 dB and 19.08 dB are observed. Experimental test results show that ACPR improves by 15.81 dB and 16.40 dB in 16QAM modulation mode and 15.44 dB and 15.82 dB in 64QAM modulation mode, respectively, which underscores the evident suppressive effect of the MP-based digital pre-distorter on the non-linearity of the ADS-based RF PAs. Consequently, digital pre-distorter based on MP model can be used for linearization of the proposed RF PAs and the pre-distortion capability is better with 16QAM modulation than 64QAM based modulation. [ABSTRACT FROM AUTHOR]

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s.

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits.

In this paper, a hybrid predistortion architecture that allies the benefits of digital and analog predistortion techniques is investigated. This Hybrid Digital/Analog Predistorter (H-DAPD) uses a low order memory polynomial as a baseband digital predistortion function. To alleviate the complexity of the analog predistorter implementation, an amplitude only predistortion function is adopted. The analog predistortion function is used to perform a coarse linearization of the amplifier. The digital predistortion function is then applied to linearize the system made of the analog predistorter and the power amplifier. The H-DAPD predistortion system is found to be superior to purely analog and to purely digital predistortion systems by combining key advantages present in each of these systems. The robustness of the proposed H-DAPD with respect to the hardware impairments that are typically associated with the implementation of analog predistortion is thoroughly discussed, and shown to be superior to that of conventional hybrid and analog predistortion systems. Experimental validation performed on a commercial power amplifier prototype using 5G new radio (NR) test signals demonstrates the enhanced robustness of the proposed H-DAPD to hardware impairments. It was found that standard compliant adjacent channel leakage ratio is obtained with up to 0.3dB resolution in the analog predistorter’s AM/AM function, and up to 3ns delay misalignment between the input and control signals of the analog predistorter. The proposed system is expected to pave the road for future hybrid predistortion systems.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A modified optimized magnitude‐selective affine (OMSA) model‐based digital predistortion (DPD) is presented that introduces the weighting function into our earlier proposed magnitude‐selective affine (MSA) method with an aim to further reduce the complexity overheads without affecting performance compared to the MSA method. This model utilizes a power‐reliant weighted function rather than the summation of MSA quantities for improving the multiband 5G new radio (NR) analog radio over fiber system performance. The OMSA‐DPD method is tested using 5G NR signals which are transmitted over a 10‐km fiber length. The performance of the OMSA‐DPD method is assessed in comparison to MSA and generalized memory polynomial (GMP) methods in terms of adjacent channel power ratio, error vector magnitude, and complexity. The experimental results show that the OMSA‐DPD method achieves better performance with lower complexity compared to the MSA and GMP models, meeting the 3GPP limits. [ABSTRACT FROM AUTHOR]

Despite their many advantages over other communication links, radio over fiber (RoF) links also suffer from nonlinearity. This limits the dynamic range performance of the RoF link. Linearization techniques like digital predistortion can be explored for mitigating this nonlinearity. A hybrid optimization algorithm called hybrid Memetic algorithm, which uses the benefits of both Tabu search algorithm and Memetic algorithm, is used for parameter estimation of the digital predistorter. Simulation results demonstrate that the hybrid Memetic algorithm offers very promising outcomes. [ABSTRACT FROM AUTHOR]

In this work, a new adaptive digital predistorter (DPD) is proposed to linearize radio frequency power amplifiers (PA). The DPD structure is composed of two sub-models. A Feedback–Wiener sub-model, describing the main inverse nonlinearities of the PA, combined with a second sub-model based on a memory polynomial (MP) model. The interest of this structure is that only the MP model is identified in real time to compensate deviations from the initial behavior and thus further improve the linearization. The identification architecture combines offline measurement and online parameter estimation with small number of coefficients in the MP sub-model to track the changes in the PA characteristics. The proposed structure is used to linearize a class AB 75 W PA, designed by Telerad society for aeronautical communications in Ultra High Frequency (UHF) / Very High Frequency (VHF) bands. The obtained results, in terms of identification of optimal DPD and the performances of the digital processing, show a good trade-off between linearization performances and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient system approach to improve the power efficiency of dual-input Doherty power amplifier (DIDPA) with maintaining its linearity level. An auto-tuning process based on a hybrid heuristic search control (HHSC) is applied to optimally define DIDPA configuration by optimizing its free parameters. Digital predistortion (DPD) is then integrated to linearize DIDPA using an optimal reduced-complexity model based on the segmentation approach. An optimal pruning process of the free parameters, based on hill-climbing (HC) heuristics, is proposed to reduce the HHSC complexity in order to refine the optimal DIDPA configuration. The system approach has been approved by experimental results, in different scenarios, using an LTE 20 MHz signal with a PAPR of 8 dB. DPD linearization under optimal DIDPA configuration improved linearity using a low-complex model with only 30 coefficients, which exhibited an error vector magnitude (EVM) of 2.5% and an adjacent channel power ratio (ACPR) of −50 dB at an averaged output power of 34 dBm. By updating the cost function coefficients and pruning the free parameters, DIDPA exhibited an EVM of 3%, an ACPR of −50 dB, and a drain efficiency of 47% at an average output power of 39 dBm.

Recently, new emerging techniques of neuromorphic hardware render spiking neuron networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With the idea of physics informed neural network, the structure can be simple while training data can be light. However its application in RF telecommunication system is still challenging. This paper, as the first time in the literatures, proposes a solution of SNN-based digital predistortion (SNN-DPD) for linearization of RF transmitters, such as power amplifiers (PA). A two-layer SNN is deployed in frequency domain to process the spectrum of the stimulus for a predistorted signal. The proposed technique is experimentally validated on a test bench with a real PA of different bias voltages. We also test the proposed SNN-DPD for multi-band linearization. The proposed method reaches the best performance of traditional DPD methods while owing advantages of the SNN, such as low power consumption and good biomimicry for AI.

This paper proposes a neural networks predistorter based on the bidirectional long-short-term memory (BiLSTM) structure. The proposed predistorter was trained while ensuring that it captures the full intrinsic behavior of the device under test including its memory effects and nonlinear distortions. For this purpose, the device under test was characterized while operating at peak power level with a test signal that emulates strong memory effects. Extensive experimental validation carried on a commercial Gallium Nitride power amplifier prototype demonstrated the ability of the proposed predistorter to maintain standard compliant adjacent channel leakage ratio over a wide range of operating conditions including operating average power, signal bandwidth, and carriers’ configurations. It has been shown that a digital predistorter (DPD) derived from one single training condition was able to linearize the device under test for 72 different test conditions with signal bandwidths between 10MHz and 40MHz, and an operating power range of 5dB. Furthermore, benchmarking results showed that the BiLSTM DPD is unable to maintain satisfactory performance when trained with a sub-optimal signal which does not emulate the full behavior of the device under test. Moreover, it has been shown that the use of the optimal characterization signal along with a generalized memory polynomial predistorter does not lead to satisfactory performance. Hence, the resilience of the predistorter is obtained by combining the suitable model structure along with the appropriate training approach. Such resilient DPD presents a paradigm shift in predistortion techniques which significantly minimizes the need for update. It is anticipated that this work will pave the road for a new generation of DPDs resilient to a wide range of operating conditions.

This paper presents a method for increasing the operating frequency range of a sub-6 GHz transmitter to millimeter wave (mm-wave) using two cascaded frequency doublers. The usage of such a frequency up-conversion approach is constrained since applying the first frequency doubler’s generated harmonics to the second doubler for mm-wave signal creation results in severe distortions and bandwidth expansion of the transmitted signal, limiting the use of such frequency up-conversion method. We have proposed a combined gain and phase retarder and physically inspired cascaded feed-forward neural network (CFFNN) based digital predistortion to mitigate the bandwidth expansion and distortions. A comparative study of feed-forward neural networks (FFNN) and CFFNN has been carried out to establish CFFNN as a significant and less complex model for effective DPD of frequency quadrupler. For proof of concept, we have experimentally validated this method for Long-Term Evolution (LTE) 20 MHZ (64-QAM) signal at 24 GHz frequency band using commercially available frequency multipliers. The measurement results show the bandwidth expansion is reduced approximately 11–12 times, and the adjacent channel power ratio (ACPR) of around −45.1 dBc and error vector magnitude (EVM) of approximately 3.07% is achieved.

Solid-state power amplifiers using semiconductor technology have the advantages of small size and high stability, and have replaced traditional travelling wave tube amplifiers in many microwave applications. Among all types of semiconductor materials, the third-generation semiconductor material GaN(Gallium Nitride) has been widely used in power amplifiers because of its advantages of wide band gap, high electron mobility, and high breakdown voltage. Based on the development of power amplifiers, this study describes the development history of solid-state power amplifiers, summarizes the performance comparison between GaN technology and other semiconductor technologies, and focuses on power amplifiers using GaN HEMT(GaN High Electron Mobility Transistor) technology. This study discusses various types of GaN HEMT power amplifiers, including class A, B, C, D, and E, etc., introduces the efficiency and linearity improvement techniques applied to GaN power amplifiers, including Doherty power amplifiers and envelopes tracking technology, and digital pre-distortion technology, etc., and makes a summary and comparison of related technologies. [ABSTRACT FROM AUTHOR]

It 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%. [ABSTRACT FROM AUTHOR]

In this letter, we address the problem of combating the effect of the nonlinearities of the transmit–receive chain in solid-state radars, which are typically generated by the power amplifier (PA) in transmission. This issue is of particular importance in meteorological radar applications, as they require to transmit pulse featuring very low autocorrelation sidelobes, which are significantly raised due to nonlinearities. In this work, we describe an adaptive predistortion design method based on an iterative approach in the spectral domain. The proposed method has been validated by means of hardware using a customized software radio and driving an amplifier into saturation. Two different ideal, highly performing pulse compression waveforms, expressly designed for weather radar applications, were sent to its input. The experimental results demonstrate the benefits of the proposed approach to design the digital predistortion module. [ABSTRACT FROM AUTHOR]

The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as a real-valued time-delay neural network (RVTDNN), an augmented real-valued time-delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no block RAMs compared to the DNN with similar accuracy. We experimentally validated this computationally efficient and memory-saving FPGA implementation of the SSCNN for digital predistortion of radio-frequency (RF) power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Radio frequency (RF) power amplifiers (PA) are efficiently linearized by adaptive digital predistortion (DPD). However, performance of DPD is severely degraded in presence of transmitter’s frequency-selective in-phase/quadrature (I/Q) imbalance. We propose DPD/IQ method that compensates the effects of transmitter I/Q imbalance and PA nonlinearity, which is dedicated for implementation in low-cost software radio defined (SDR) cellular base stations. The advantage of DPD/IQ is low complexity in terms of reduced number of DPD coefficients which provides significant savings of FPGA resources. The DPD/IQ has been evaluated after method has been implemented in SDR board. Measured results clearly demonstrate efficient compensation of PA and transceiver impairments enabling transmission of wide bandwidth waveforms and realization of sophisticated modulation schemes. Improvement in image rejection ratio of 10-15dB is achieved. Considering compensation of PA nonlinearities, the ACPR at PA output is decreased by 15dBc.

This study describes an experimental realization using digital predistortion (DPD) for a fifth generation (5G) multiband new radio (NR) optical front haul (OFH) based Radio over Fiber (RoF) link. For the performance enhancement and complexity reduction of RoF links, a novel Convolutional Neural Network (CNN) based DPD technique is proposed, followed by comparisons with the generalised memory polynomial (GMP) based DPD method. To support enhanced mobile broad band scenario, the experimental testbed uses the 5G NR waveforms at 10 GHz with 20 MHz bandwidth and a flexible-waveform signal at 3 GHz with 20 MHz bandwidth. For 10 km of typical single mode fiber, a Mach Zehnder Modulator with two distinct radio frequency waveforms modulates a 1310 nm optical carrier utilizing distributed feedback laser. The error vector magnitude and number of estimated coefficients, and multiplications are all used to describe the experimental outcomes. The goal of the research is to see if CNN-based DPD improves performance while lowering complexity levels to meet 3GPP Release 17 criteria.

This paper proposes a novel predistorter architecture suitable for hybrid baseband/RF implementation. The distortion function is split into a memory polynomial and an AM/AM only look-up table (LUT). The memory polynomial function is to be applied in the baseband digital domain whereas the LUT predistorter is suitable for low complexity implementation in either the baseband digital or the RF analog domains. The proposed model was compared to benchmark models using 20MHz and 40MHz wide 5G test signals, and found to be superior in terms of adjacent channel leakage ratio performance and complexity. Experimental results showed that the model can achieve ACLR levels of better than −50dBC with a relatively low number of coefficients. The proposed model requires around 20% less coefficients than the reverse twin-nonlinear two-box model, and achieves slightly better ACLR while easing the analog implementation of the static nonlinearity predistortion function. Most importantly, the low-complexity hybrid implementation of the proposed model can drastically reduce the speed requirements in the digital to analog converter of the signal transmit path by up to 80% when compared to traditional DPD systems.

In multiband transmitters where two or more radios share the same power amplifier (PA), interferences can usually be kept below an acceptable level with the help of multi-band digital predistortion (DPD) techniques. This article reports on novel frequency scenarios in dual-band transmitters where additional cross-modulation products not reported before, are found to overlap and interfer with both the lower and upper-band signals. These cross-modulation products arise in-band when the frequency separation between the bands is close to a sub-harmonic of the band frequencies. The leading nonlinear cross-modulation products are identified for various frequency plans and found to be of increasing order as the frequency interval between the two carriers shrinks. A PA model featuring the four leading cross-modulation products is proposed and found to exhibit an accuracy close to the measurement noise floor when applied to three different PAs. The additional in-band distortions generated by the PA under such operation cannot be filtered or compensated using conventional dual-band DPD. When using the proposed model architecture with the indirect-learning predistortion methodology for linearizing dual-band PAs, DPD is found experimentally to improve the ACPR and EVM by 25 dBc and 11 percentage points, respectively. These and the other experimental results reported indicate that the proposed linearization algorithm provides a suitable method with high efficacy for these special dual-band scenarios.

In this paper, an efficient neural-network-based adaptive DPD design which performs well under power varying conditions is presented. The DPD design is derived on the basis of the envelop time-delay neural network (ETDNN). The redefined ETDNN-DPD requires the part of parameter updates, which enables to adapt it to the rapid change of power amplifier (PA) distortion. Additionally, the redefined ETDNN-DPD also maintains the stability of the compensation performances under varying power condition while its structure is pruned by the structured pruning. Furthermore, to verify its practical use, we also propose the weight scaling technique, which reduces multiplications of the redefined ETDNN-DPD, and applied it to the implementation of the redefined ETDNN-DPD on FPGA. Compared FPGA-implemented ETDNN-DPD with FPGA-implemented conventional memory polynomial DPD, we verified that our proposed DPD achieved 3.2 dB better error vector magnitude (EVM) while lower hardware resource utilization at the fixed power level. Moreover, our proposed DPD kept better performance under the varying power condition only by the partial update of its parameters than memory polynomial DPD.

In this article, a sparse-Bayesian treatment is proposed to solve the crucial questions posed by power amplifier (PA) and digital predistorter (DPD) modeling. To learn a model, the advanced Bayesian framework includes a group of specific processes that maximize the likelihood of the measured data: regressor pursuit and identification, coefficient estimation, stopping criterion, and regressor deselection. The relevance vector machine (RVM) method is reformulated theoretically to be implemented in complex-valued linear regression. In essence, given an initial set of candidate regressors, the result of this sparse-Bayesian learning approach is the most likely model. Experimental results are provided for the linearization of class AB and class J PAs driven by a 30-MHz fifth-generation new radio signal for a fixed average power, where the evolution of the figures of merit versus the number of active coefficients is examined for the proposed sparse-Bayesian pursuit (SBP) algorithm in comparison to other greedy algorithms. The SBP presents a good performance in terms of linearization capabilities and computational cost. Furthermore, the proposed Bayesian framework enabled the design of a DPD model structure, deselect regressors, and readjust coefficients in a direct learning architecture, demonstrating the robustness to changes in the power level over a 10-dB range. [ABSTRACT FROM AUTHOR]

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 et al., 2019), the proposed models reduce the per-sample complexity and required memory by $94\hbox{--}99$ %, 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. [ABSTRACT FROM AUTHOR]

A novel 7.2 dB output power back off Doherty power amplifier (DPA) based on asymmetrical load is proposed using two same transistors in this paper. To get maximum efficiency at back‐off output power and the largest saturate output power level simultaneously, the carrier power amplifier (PA) is matched to the maximum efficiency region when peaking PA is almost off, and both of the two PAs are matched to maximum power region to get largest output power. According to the proposed design methodology, high efficiency at back‐off power and high peak power DPA is designed and fabricated with two Cree's GaN HEMT CGH40010F devices. Measurements results show that the saturated output power of the DPA is over 44.5 dBm, the drain efficiency when 7.2 dB output power back off and saturate power are above 42% and 53%, respectively, in the range of 3.4–3.8 GHz. Meanwhile, better than −47.8 dBc adjacent channel leak power ratio can be achieved with digital predistortion under 20 MHz LTE signal testing at 3.4 GHz, the final design size is only 14 × 12 cm2. [ABSTRACT FROM AUTHOR]

This article presents two novel digital predistortion (DPD) based architectures that jointly mitigate the inphase/quadrature (IQ) modulator impairments and the power amplifier (PA) nonlinear distortion in wireless transmitters. The proposed architectures are multibit cartesian and complex delta-sigma modulator-based joint DPDs, called CDSM-JDPD and CXDSM-JDPD, respectively, which enable using low-cost digital-to-analog converters (DACs) while offering versatile linearization capabilities to combat the coexisting distortions of the PA and the IQ modulator. The proposed approach alleviates the need for reverse modeling and implementation of extra hardware to separately deal with frequency-dependent IQ impairments. Moreover, the CXDSM-JDPD enhances the linearization performance and relaxes the high oversampling ratio (OSR) requirement by quantizing the signal more efficiently. Furthermore, the presented concepts inherently support the use of low-resolution DACs, which offers a tremendous advantage in designing and implementing low-cost and energy-efficient radio transmitters. Extensive set of hardware-in-the-loop RF verification measurements with a commercial PA are provided, including two timely 5G New Radio (NR) scenarios at NR bands n3 and n78, while covering channel bandwidths up to 100 MHz and varying the OSR and the DAC bit resolution. The obtained results demonstrate the excellent linearization capabilities of the proposed solutions and their superiority compared to other DSM-based DPD approaches. [ABSTRACT FROM AUTHOR]

With the increasing demand for large signal bandwidth in communication systems, the complexity of the behavioral model of power amplifier (PA) will also increase. In this paper, a modified weighted magnitude‐selective model based on magnitude‐selective affine (MSA) function based model and weighted memory polynomial model is proposed to ensure low complexity and good modeling performance under broadband signal excitation. This model is constructed by using the power‐dependent weighted function instead of the sum of multiple MSA functions in MSA. It can not only be used to solve the nonlinearity of PA excited by large bandwidth signal but also reducing the model coefficients without affecting the modeling performance makes the digital predistortion of large bandwidth signals more simple and effective. In this paper, a 100 MHz broadband signal is used for simulation and experiment on a wideband PA. The results show that for the same performance the WMA model required approximately 80% less coefficients. [ABSTRACT FROM AUTHOR]

Frequency-selective (FS) digital predistortion (DPD), which focuses on suppressing the nonlinear distortion at a specific band, has gained increased interest in recent years. However, there is a lack of a general FS DPD approach that is applicable to most scenarios and is suitable for practical implementation. This paper proposes an FS DPD method based on a generalized indirect learning architecture (GILA). Our work on this GILA-based FS DPD method is composed of two contributions. First, an FS Volterra series model structure, which consists of a basic linear part and a partial-band pre-compensation part, is proposed to construct an FS predistorter. Second, a GILA-based identification method is proposed to extract coefficients of the FS predistorter with the FS Volterra series model structure. The proposed GILA-based FS DPD is a general method with low complexity for implementation. Both simulations and experiments verify that the target band of the DPD linearization is arbitrary for the proposed GILA-based FS DPD method. As a special case, when using the proposed method to focus on suppressing the out-of-band distortion, the adjacent channel power ratio performance of the proposed method can be 7.1 dB better than that of the conventional full-band DPD. [ABSTRACT FROM AUTHOR]

A high‐accuracy filtering‐based envelope generation technique for wideband envelope tracking power amplifiers (ET PAs) is proposed to improve PA efficiency with limited envelope bandwidth. It is achieved by optimizing the envelope generation block by employing three generalized rectification functions. The proposed envelope can be obtained by finding the proper coefficients in these generalized rectification functions. Furthermore, the digital predistortion can be employed to compensate for the nonlinearity and memory effect introduced by the proposed envelope. Experiments have been carried out on an ET system that is operated with the center frequency of 3.5 GHz, which have shown that the proposed technique can improve the PA efficiency by more than 2.2% with only 1.2× the modulated bandwidth compared to the state‐of‐the‐art methods. The normalized mean square error can achieve −44.8 dB, and the adjacent channel leakage ratio can achieve better than −52 dBc. The proposed technique can improve the PA efficiency with limited envelope bandwidth compared to the state‐of‐the‐art techniques, and the PA supplied by the proposed envelope can be linearized well, while it may cost more resources to find the proper coefficients of these three generalized rectification functions. [ABSTRACT FROM AUTHOR]

In this letter, a behavior model, namely CP- LSTM, composed of Chebyshev polynomials (CP) and a long short-term memory (LSTM) network is built to linearize the wideband millimeter-wave power amplifier (mmW PA) in the fifth-generation (5G) mobile communication system. In order to verify the linearization performance of the CP- LSTM predistorter, a 100-MHz bandwidth 5G new radio (5G NR) signal is employed to test the 28-GHz mmW PA under the text. Experimental results show that the adjacent channel power ratio (ACPR) of the mmW PA with the CP- LSTM can be improved by 20 dB which is 5-dB better than with the LSTM and 3-dB better than with the generalized memory polynomial (GMP). Therefore, the proposed CP- LSTM model is very effective to linearize 5G mmW PAs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient approach for optimizing the decomposed vector rotation (DVR) model for digital predistortion (DPD). The DVR model’s basis functions are constructed piecewise by dividing the input space into segments bounded by thresholds. This paper investigates how to set the thresholds optimally using an iterative approach based on the decomposition of the global optimization problem into a set of unimodal sub-problems so that a unidirectional minimization can be used to optimize the positions of thresholds. The proposed approach has been evaluated using measurements from a real power amplifier (PA). The experimental results illustrate the efficiency of the proposed optimization approach and show that the thresholds’ optimization improves linearization performances significantly compared to conventional DVR with uniform segmentation.

In this paper, an efficient digital predistortion (DPD) architecture utilizing over-the-air (OTA) feedback is presented to linearize phased array transmitters. It places one additional observation antenna (OA) in the far field to capture the combined signal of the array outputs and uses the combined signal to build a nonlinear model that describes the joint nonlinear distortions of all the power amplifiers (PAs) in the phased array. Then, a corresponding DPD model is extracted by the typical indirect learning method to linearize the phased array. After introducing this DPD architecture, the relationship between the OA position and the performance of the extracted DPD model is specifically explored, and two significant results are found. First, when the OA is perfectly colocated in the main beam direction, the DPD coefficients extracted with an arbitrary steering angle for the phased array are also applicable to any other steering angle. Second, when the OA is not perfectly placed in the main beam direction, the linearization performance for the extracted DPD coefficients will degrade as the OA placement mismatch increases, and the phased array has an optimum steering angle at which the OA placement mismatch has a minimal influence on the DPD performance. Based on the above two results, a new DPD training strategy is proposed, which extracts only one set of DPD coefficients by making the phased array point at the optimum steering angle. The advantages of the proposed strategy are that the OA can be placed in a relatively wide range of positions and that the extracted coefficients will be applicable to any other steering angle. The two results and the proposed DPD training strategy are verified by simulations, which are conducted on a 1 × 4 uniform linear array (ULA) and a 4 × 4 uniform rectangular array (URA).

Power amplifiers (PAs) are widely used in RF broadcasting applications. However, they exhibit nonlinear behavior and deteriorate the quality of transmitted signals. Digital predistortion (DPD) is developed to linearize the distortion generated by PAs. Due to the contradiction between modeling precision and complexity, to reduce the complexity and ensure comparable performance, it is necessary to determine an appropriate or optimized structure before applying DPD. Heuristic method is a good method to solve such multivariate problems with a considerable large search domain. In this paper, a novel approach to determine the structure of DPD based on heuristic method is proposed, including the enhanced hill-climbing (EHC) with stronger global search ability and enhanced genetic algorithm (EGA). Ridge regression is introduced to ensure the correctness of the search direction. Besides, orthogonal matching pursuit is used to further reduce the basis number while keeping good performance. The validation of the proposed techniques on a Doherty PA using generalized memory polynomial (GMP) model demonstrates the capability to efficiently find the dimensions of an appropriate GMP model. As shown by the convergence curve and the final model performance, the model searched by the proposed method has satisfactory results, and achieves a good balance between complexity and performance.

This paper proposes a new digital predistortion (DPD) scheme for linearizing millimeter-wave hybrid beamforming transmitters using observation receivers with low-bit resolution analog-to-digital converters (ADCs). To train the DPD function required to compensate for the distortions exhibited by a given sub-array (also called main sub-array), an error signal is produced by out-of-phase combining the main sub-array transmitted signal and the one generated by another sub-array (also called auxiliary sub-array) using anti-beamforming modules. The error signal is then frequency down-converted and digitized using a low-bit resolution ADC. Proof-of-concept validation experiments are conducted by applying the proposed DPD system to linearize an off-the-shelf hybrid-beamforming array comprised of four 64-element sub-arrays, operating at 28 GHz and driven with up to 800 MHz orthogonal frequency-division multiplexing modulated signals. Using the proposed DPD scheme, an observation receiver with a 4-bit ADC was sufficient to improve the adjacent channel power ratio by 10 dB and the error vector magnitude was reduced from 5.8% to 1.6%. These results are similar to those obtained using an observation receiver with 16-bit ADC.

This paper presents an envelope linearization technique to compensate for the nonlinear distortion of envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed envelope optimization (EOPT) method is agnostic of the nonlinear distortion generated in the envelope supply path and can compensate for the nonlinear distortion at the ET PA output without the need to monitor the output at the envelope tracking modulator (ETM). The linearization system in the envelope path is based on the envelope generalized memory polynomial (EGMP) behavioral model. Since the ETM output is not available, an iterative nonlinear least squares solution inspired in the deep deterministic policy gradient (DDPG) algorithm is proposed to extract the coefficients of the EGMP model. The EOPT method is validated on a system-on-chip (SoC) ET PA board designed for mobile terminal applications. Experimental results show the suitability of the proposed method to guarantee the linearity requirements (i.e., adjacent channel power ratio below −36 dBc) with 16.8% of power efficiency when operating the ET PA with 5G new radio test signals of 60 MHz bandwidth operating at 2.55 GHz (band 7). The linearization performance of the proposed EOPT method is comparable to the envelope leakage cancellation (ELC) approach (but saving the need for an analog to digital converter to monitor the ETM output), and can outperform a conventional I-Q digital predistorter based on the generalized memory polynomial (GMP) behavioral model. [ABSTRACT FROM AUTHOR]

This paper presents a novel magnitude‐selective affine (MSA) based digital predistortion (DPD) method for the performance investigation of multiband 5G new radio (NR) based analog radio over fiber link. The proposed MSA‐DPD method is derived from the canonical piecewise linear (CPWL) based model by employing MSA functions, which can result in reduction of the number of multiplication operations and the model complexity. The 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible‐waveform signal at 3 GHz with 20 MHz bandwidth is used. A dual drive Mach Zehnder modulator having two distinct RF signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The proposed MSA‐DPD method is compared with the CPWL and generalized memory polynomial method. The experimental results are presented in terms of adjacent channel power ratio, error vector magnitude, number of estimated coefficients and multiplications suggesting that MSA‐DPD method achieves a better performance as compared to CPWL and GMP models with much lesser complexity meeting the 3GPP Release 17 requirements. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) power amplifiers (PA) are efficiently linearized by adaptive digital predistortion (DPD). However, performance of DPD is severely degraded in presence of transmitter's frequency-selective in-phase/'quadrature (I/Q) imbalance. We propose the DPD/IQ method that compensates the effects of transmitter 's I/Q imbalance and PA nonlinearity and is dedicated for implementation in low-cost software defined radio (SDR) cellular base stations. The advantage of DPD/IQ is low complexity in terms of reduced number of DPD coefficients which provides significant savings of FPGA resources. The performance of DPD/IQ has been evaluated after the method has been implemented in SDR board. Measured results clearly demonstrate efficient compensation of PA and transceiver impairments enabling transmission of wide bandwidth waveforms and realization of sophisticated modulation schemes. Improvement in image rejection ratio of 10-15 dB is achieved. Considering compensation of PA nonlinearities, the ACPR at PA output is decreased by 15 dBc. [ABSTRACT FROM AUTHOR]

In this article, we study closed-loop digital predistortion (DPD) systems and associated learning algorithms. Specifically, we propose various low-complexity approaches to estimate and manipulate the inverse of the input data covariance matrix (CM) and combine them with the so-called self-orthogonalized (SO) learning rule. The inherent simplicity of the SO algorithm, combined with the proposed solutions, allows for remarkably reduced complexity in the DPD system while maintaining similar linearization performance compared to other state-of-the-art methods. This is demonstrated with thorough over-the-air (OTA) mmW measurement results at 28 GHz, incorporating a state-of-the-art 64-element active antenna array, and very wide channel bandwidths up to 800 MHz. In addition, complexity analyses are carried out, which together with the measured linearization performance demonstrates favorable performance–complexity tradeoffs in linearizing mmW active array transmitters through the proposed solutions. The techniques can find application in systems where the power amplifier (PA) nonlinearities are time-varying and thus frequent or even constant updating of the DPD is required, good examples being mmW adaptive antenna arrays as well as terminal transmitters in 5G and beyond networks. [ABSTRACT FROM AUTHOR]

In this paper, a signal-based digital predistortion (DPD) scheme is proposed for the linearization of power amplifiers (PAs) in broadcasting transmitters. The proposed DPD scheme is called the signal-based DPD to distinguish it from the conventional model-based DPD, where the motivation of the former is to synthesize the predistorted signal and that of the latter is to construct the preinverse function of the PA. Our work on this signal-based DPD scheme mainly consists of two parts: a signal-based predistorter and its identification. The signal-based predistorter is derived according to the typical fixed point approach associated with the contraction mapping theorem (CMT), and it has an online cascade structure that is constructed by reproducing offline iterations of the CMT. Identification methods are proposed by tackling the divergent output problem of the signal-based predistorter with a cascade structure. In particular, three coefficient extraction strategies, which have different complexities and different linearization results, are proposed to identify the signal-based predistorter. The experimental results verify that the proposed signal-based DPD scheme can achieve the best linearization performance compared with all the typical model-based DPD methods, including the indirect learning architecture-based DPD, direct learning architecture-based DPD, and iterative learning control-based DPD. [ABSTRACT FROM AUTHOR]