A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches

Pedro, José; Maas, Steve A.

doi:10.1109/tmtt.2005.845723

Cited by 370 publications

(255 citation statements)

References 38 publications

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“…1 has been used previously to derive PA models for SISO PAs [25,[27][28][29]. We extend the derivation of the SISO EEMP [25] model to concurrent dualband PAs.…”

Section: I T H E O R Ymentioning

confidence: 99%

2D Extended envelope memory polynomial model for concurrent dual-band RF transmitters

Amin

Landin

Händel

et al. 2017

Int. J. Microw. Wireless Technol.

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The paper presents a two-dimensional (2D) extended envelope memory polynomial model for concurrent dual-band radio frequency (RF) power amplifiers (PAs). The model is derived based on the physical knowledge of a dual-band RF PA. The derived model contains cross-modulation terms not included in previously published models; these terms are found to be of importance for both behavioral modeling and digital predistortion (DPD). The performance of the derived model is evaluated both as the behavioral model and DPD, and the performance is compared with state-of-the-art 2D-DPD and dual-band generalized memory polynomial (DB-GMP) models. Experimental result shows that the proposed model resulted in normalized mean square error of 251.7/251.6 dB and adjacent channel error power ratio of 263.1/263.4 dB, for channel 1/2, whereas the 2D-DPD resulted in the largest model error and DB-GMP resulted in model parameters that are three times more than those resulted with the proposed model with the same performance. As pre-distorter, the proposed model resulted in adjacent channel power ratio of 255.8/254.6 dB for channel 1/2 and is 7-10 dB lower than those resulted with the 2D-DPD model and 2-4 dB lower compared with the DB-GMP model. I . I N T R O D U C T I O NGrowing demand for higher data rates along with the global concern on green technologies places challenging requirements on wireless transmitters. To accommodate higher data rates, significant efforts have been made to design multiband transmitters to support multiple standards in different frequency bands. More specifically, dual-band radiofrequency (RF) power amplifiers (PAs) that can accommodate signals in two different frequency bands have been successfully designed and tested [1][2][3].The non-linear behavior of RF PAs in the transmitter chain causes well-known hardware impairments. The non-linear distortion, which typically is dynamic, contains both in-band distortion and spectral regrowth, i.e. spectral spreading into the adjacent bands, thus interfering with signals in these channels. There are numerous publications on the linearization and efficiency improvement of single-input-singleoutput (SISO) RF PAs [4][5][6]. Digital predistortion (DPD) is a well-known technique and is applied to compensate PA nonlinear behavior and to improve the overall power efficiency of the RF transmitters. In DPD, an inverse mathematical model of the PA is used and put upstream of the PA to produce a linear output signal. Thus, an accurate mathematical model is required for the linearization of RF PAs.When dealing with the concurrent dual-band RF PAs, SISO DPD methods cannot be used, since there are two input and two output signals. In multi-band PAs, the nonlinear distortions are classified as in-band and cross-band distortions [7][8][9][10]. Both these distortion types should be included in behavioral models and DPD techniques for modeling and linearizing concurrent multi-band PAs.In recent years, several techniques have been proposed to compensate the non-linear distortions in con...

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“…1 has been used previously to derive PA models for SISO PAs [25,[27][28][29]. We extend the derivation of the SISO EEMP [25] model to concurrent dualband PAs.…”

Section: I T H E O R Ymentioning

confidence: 99%

2D Extended envelope memory polynomial model for concurrent dual-band RF transmitters

Amin

Landin

Händel

et al. 2017

Int. J. Microw. Wireless Technol.

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“…The most evident nonlinear behavior is the gain compression that occurs when the PA is driven at higher power levels. Besides, different kinds of dynamic effects are also observable, including low-frequency memory effects (on the order of the envelope bandwidth) associated to active-device self-heating, biasing circuitry and trapping effects [17,18], as well as high-frequency memory effects (on the order of the carrier frequency) attributed to non-uniform frequency response of the matching networks [6].…”

Section: Power Amplifier Behavioral Modelingmentioning

confidence: 99%

“…For linearization purposes, an accurate and low complexity nonlinear dynamic model for the PA is required [6]. PA models with different levels of internal description and computational complexity can be found.…”

Section: Power Amplifier Behavioral Modelingmentioning

confidence: 99%

“…Behavioral models are classified based on the nature of the signals involved [6]. If they relate the bandpass signals at the PA input and output, they are called bandpass behavioral models.…”

Section: Power Amplifier Behavioral Modelingmentioning

confidence: 99%

“…Among the available strategies, digital baseband predistortion (DPD) is a cost-effective linearization solution [5]. The successful design of a DPD scheme relies strongly on the availability of a low complexity and high accurate low-pass equivalent behavioral model for the PA [6]. In literature, there are various techniques that can provide an adequate mathematical description for the PA behavior.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Freire¹,

França²,

Lima³

2014

PIER C

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Abstract-Feed-forward artificial neural networks (ANNs) can provide the adequate model required for the linearization of power amplifiers (PAs) used in wireless communication systems. A common characteristic of previously available ANN-based models for linearization purposes is the use of a single real-valued ANN having two outputs. The contribution of this work is to report the benefits of performing such behavioral modeling based on two independent real-valued ANNs, where each network has a unique output. The proposed ANN-based model is applied to the behavioral modeling of a GaN HEMT class AB PA, and its accuracy is compared to previous approaches in two different scenarios. First, in case of similar number of network parameters, it is observed that the proposed ANN-based model can reduce the normalized mean-square error (NMSE) by up to 1.3 dB. Second, in a situation of comparable modeling accuracy (NMSE = −40 dB), it is observed that the proposed ANN-based model can reduce the number of network parameters by up to 40% (from 62 to 38 real-valued parameters).

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Intermodulation

Pedro

2024

Encyclopedia of RF and Microwave Engineering

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This article presents a broad overview of intermodulation distortion (IMD) in microwave and RF circuits and systems. It starts by putting IMD in the context of the linear and nonlinear perturbations of telecommunication systems to then explain its physical origins and most common theoretical analysis methods and laboratory characterization setups. For that, several device/system models and analytical and numerical techniques are addressed as well as the nonlinear distortion generation mechanisms present in typical microwave small‐signal amplifiers, high‐power amplifiers, and frequency converters are reviewed.

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A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches

Cited by 370 publications

References 38 publications

2D Extended envelope memory polynomial model for concurrent dual-band RF transmitters

2D Extended envelope memory polynomial model for concurrent dual-band RF transmitters

Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Intermodulation

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