A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Morgan, D.R.; Ma, Zhengxiang; Kim, Jaehyeong; Zierdt, Mike; Pastalan, J.

doi:10.1109/tsp.2006.879264

Cited by 1,212 publications

(760 citation statements)

References 24 publications

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“…The structure of a linearization was frequently explained in literature [10,[18][19][20][21][22][23]. Briefly, a DPD system is modeled as a black box, namely a Pre-distorter (PD) having nonlinear characteristic, which is cascade connected to the PA.…”

Section: Digital Predistortionmentioning

confidence: 99%

Effect of the Base-band Measurement Setup Errors on DPD Performance and Elimination Procedure

Yüzer

Şanlı

2017

AEM

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In this study, the effect of base-band measurement setup errors on DPD performance was investigated and a calibration procedure is developed to eliminate the measurement errors. A base-band measurement setup is prepared at laboratory with instruments and then the data which is measured and the deteriorating effect of errors on Digital Predistortion (DPD) linearization performance are investigated. In order to eliminate deteriorating effect of this error a three steps calibration procedure is developed. Before and after calibration application DPD performance is measured. It is showed both in simulation and experimentally that the calibration procedure improved the DPD system linearization performance from 10 dB to 26dB and 13dB to 20dB, respectively.

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Section: Digital Predistortionmentioning

confidence: 99%

Effect of the Base-band Measurement Setup Errors on DPD Performance and Elimination Procedure

Yüzer

Şanlı

2017

AEM

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“…Therefore, it is generally simplified into Wiener, Hammerstein, WienerHammerstein or generalized memory polynomial models (GM P DP D ) [20]. GM P DP D reduces Volterra's model complexity [20][21][22][23][24]. For this work, indirect learning architecture has been used to construct the GM P DP D , which eliminated the need for a model assumption and parameter estimation of the power amplifier [21,22].…”

Section: Generalized Memory Polynomial Vs Volterra Modelsmentioning

confidence: 99%

“…GM P DP D reduces Volterra's model complexity [20][21][22][23][24]. For this work, indirect learning architecture has been used to construct the GM P DP D , which eliminated the need for a model assumption and parameter estimation of the power amplifier [21,22]. Moreover, GM P DP D is more robust and its coefficients can be easily estimated using a least square algorithm [20,21].…”

Section: Generalized Memory Polynomial Vs Volterra Modelsmentioning

confidence: 99%

Enhancing Power Efficiency of Doherty Power Amplifiers Using Windowing Based Crest Factor Reduction Technique

Vikraman¹,

Giofrè²,

Colantonio³

2016

PIER C

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Abstract-This paper investigates the performance of a Windowing Based Crest Factor Reduction (CF R W B ) technique, to enhance the power efficiency of Radio Frequency (RF) power amplifiers. In particular, CF R W B is implemented on a Doherty Power Amplifier (DP A) in conjunction with Generalized Memory Polynomial (GM P DP D ), and Volterra series based Digital Predistortion (V DP D ) techniques. Key features like spectral regrowth, Peak to Average Power Ratio (P AP R) reduction, efficiency improvement and Error Vector Magnitude (EV M ) have been used to measure the efficacy of the proposed method. Both simulation and experimental results show that the proposed combination of CF R W B technique with GM P DP D and V DP D is able to reduce the P AP R of the complex input signals by nearly 60%, with minimal degrading of the EV M and spectral regrowth. Moreover, such signal with reduced P AP R can be used to overdrive the DPA, allowing for a relevant average efficiency enhancement (i.e., up to 25%), while fulfilling the requirements of modern communication standards such as Wideband Code Division Multiple Access (W CDM A) and long-term evolution (LT E).

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“…7(a) and 7(b), respectively. The model dimensions calculated, using the proposed algorithm and the convergence criteria defined in (8), for each of the considered test signals are summarized in Table 2. These results clearly illustrate the anticipated increase in the memory depth of the system as the bandwidth of the drive signal increases.…”

Section: The Nmse [Nmse Db (N )] As Well As the Nmse Improvement [∆Nmentioning

confidence: 99%

Performance-Driven Dimension Estimation of Memory Polynomial Behavioral Models for Wireless Transmitters and Power Amplifiers

Hammi

Younes²,

Kwan³

et al. 2010

PIER C

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Abstract-A novel approach is proposed for automated dimension estimation in memory polynomial based power amplifiers/transmitters behavioral models. This method consists of successively identifying the static nonlinearity order and memory depth of the model in accordance with a predefined performance criterion. The proposed method is validated using a 3G Doherty power amplifier driven by various WCDMA signals. Experimental results demonstrate the robustness of the proposed successive sweep approach compared to the conventional blind simultaneous sweep approach.The proposed dimension estimation method is an enabling tool for efficient design optimization of power amplifiers circuits to enhance their linearizability.Corresponding author: O. Hammi (ohammi@ucalgary.ca). 174Hammi et al.

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A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Cited by 1,212 publications

References 24 publications

Effect of the Base-band Measurement Setup Errors on DPD Performance and Elimination Procedure

Effect of the Base-band Measurement Setup Errors on DPD Performance and Elimination Procedure

Enhancing Power Efficiency of Doherty Power Amplifiers Using Windowing Based Crest Factor Reduction Technique

Performance-Driven Dimension Estimation of Memory Polynomial Behavioral Models for Wireless Transmitters and Power Amplifiers

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