A comparative analysis of behavioral models for RF power amplifiers

Isaksson, Magnus; Wisell, David; Rönnow, Daniel

doi:10.1109/tmtt.2005.860500

Cited by 312 publications

(200 citation statements)

References 32 publications

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“…The difference between measured and estimated complex-valued output envelopes gives the error signal. The accuracies of PA behavioral models are assessed by the normalized mean-square error (NMSE), as defined in [21]. The device under test (DUT) is a GaN HEMT class AB PA.…”

Section: Experimental Validationmentioning

confidence: 99%

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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Section: Experimental Validationmentioning

confidence: 99%

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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“…It is an extension of the SISO parallel Hammerstein (PH) model [17] to concurrent dual-band PAs. The extension was made by introducing two input signals to the SISO PH model and expanding to the frequency bands of interest.…”

Section: Introductionmentioning

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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“…Another reduction method is proposed in [4], which uses the derivatives of the state variable to form an orthogonal projection matrix, and then reduce the original system by projection with this orthogonal matrix. There are also some other nonlinear model order reduction methods such as the trajectory piecewise linear method in [5]. In this paper another method to achieve linearization is presented as Orthogonalized Nonlinear Model for nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Housing Society Management, Authentication and Security App

Bhavani.A¹

2017

ijecs

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Abstract---It is important to understand the architecture of Non linear systems and the components that are responsible for introducing nonlinear distortion. In order to evaluate the effect of nonlinear distortion on system performance the modeling and simulation is the requirement of present scenario of modern wireless communication systems. We have presented the orthogonalization of the behavioral model is beneficial for the better prediction of nonlinear distortion and extracts the uncorrelated component of the nonlinear output that is responsible for the degradation of system performance. Orthogonalization of power series nonlinear model is performed. Nonlinearity in wireless communication system is mainly introduced by nonlinear devices which are organized in the design of the transmitter and the receiver. In order to figure out the effect of nonlinear distortion on system performance, it is important to understand the architecture of these systems and the components that are responsible for introducing nonlinear distortion. It is also important to evaluate the modeling and simulation of these systems. Modeling and Simulation is an important step for an efficient design of modern wireless communication systems. Nonlinearity and nonlinear distortion is introduced by nonlinear behavior of nonlinear devices and their relationship to the performance of wireless communication system will be discussed. The main nonlinear devices that produce nonlinear distortion are power amplifiers, mixers in wireless transmitters and low noise amplifiers in wireless receivers [1]. Keywords---DownlinkA traditional method is to first linearize the system then perform model order reduction on the linear system. But it is well known that this method cannot give good approximation results. The ''quadratic reduction method'' presented is based on the idea of approximating the nonlinear system by a quadratic system through dropping the terms of more than two degree in Taylor expansion of the nonlinear term [1] . The bilinearization method proposed in [2] first approximating the original nonlinear system by a bilinear system, then doing order reduction on this approximate bilinear system by using the Volterra series representation of bilinear system in control theory. Variational equation model order reduction method in [3] is based on the variational equation theory in [1]. By this method, the original nonlinear system is changed into several linear systems, then order reduction is done on each linear system. Another reduction method is proposed in [4], which uses the derivatives of the state variable to form an orthogonal projection matrix, and then reduce the original system by projection with this orthogonal matrix. There are also some other nonlinear model order reduction methods such as the trajectory piecewise linear method in [5]. In this paper another method to achieve linearization is presented as Orthogonalized Nonlinear Model for nonlinear system. II NONLINEAR MODEL FOR ORTHOGONALIZATIONAs studied in recent years, the ortho...

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A comparative analysis of behavioral models for RF power amplifiers

Cited by 312 publications

References 32 publications

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

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

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

Housing Society Management, Authentication and Security App

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