A general evaluation criteria for behavioral power amplifier modeling

Wisell, David; Isaksson, Magnus; Keskitalo, Niclas

doi:10.1109/arftg.2007.5456311

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…However, data sampled according to the ZFGST does only allow direct evaluation of NMSE. The NMSE has earlier [14]- [15] been shown to be an inadequate metric for model performance evaluation. In fact, ACEPR was in [16] found to be the best low-complexity metric to identify nonlinear mismatches.…”

Section: Model Validationmentioning

confidence: 99%

Wideband characterization of power amplifiers using undersampling

Landin

Nader

Björsell

et al. 2009

2009 IEEE MTT-S International Microwave Symposium Digest

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In this paper a radio frequency power amplifier is measured and characterized by the use of undersampling based on the generalized Zhu-Frank sampling theorem. A test system has been designed allowing the bandwidth of the stimuli signal to be 100 MHz in the characterization process. That would not be possible with any vector signal analyzer on the market. One of the more challenging problem within the proposed concept is the model validation process. Here, two different techniques for model validation are proposed, the multitone and the spectrum scan validation methods.

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Section: Model Validationmentioning

confidence: 99%

Wideband characterization of power amplifiers using undersampling

Landin

Nader

Björsell

et al. 2009

2009 IEEE MTT-S International Microwave Symposium Digest

Self Cite

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“…In this work, the weighting function ( ) ( ) W f adopted is a soft thresholding window similar to the one defined in [12].…”

Section: Weighted Error-to-signal Power Ration (Wespr)mentioning

confidence: 99%

“…The weighted error-to-signal power ratio is a frequency domain metric proposed in [12] as an extension to the ACEPR. Indeed, in the WESPR metric, the power of the error function can be calculated in a customized manner which contrasts with the predefined standard dependent approach adopted in the ACEPR metric.…”

Section: Weighted Error-to-signal Power Ration (Wespr)mentioning

confidence: 99%

“…In frequency domain, the adjacent channel error power ratio (ACEPR), and the weighted error-tosignal power ratio (WESPR) are among the metrics commonly used to evaluate the performance of behavioral models [11] [12]. Prior work reported that, among the various performance evaluation criteria, the NMSE and the WESPR metrics are the strongest candidates for capturing the in-band and out-of-band errors, respectively [9] [12]. However, the use of these metrics for the dimension selection of memory polynomial has not been addressed.…”

Section: Introductionmentioning

confidence: 99%

“…The time domain metrics mainly include the normalized mean squared error (NMSE), the memory effects ratio (MER), and the memory effects modeling ratio (MEMR) [9] [10]. In frequency domain, the adjacent channel error power ratio (ACEPR), and the weighted error-tosignal power ratio (WESPR) are among the metrics commonly used to evaluate the performance of behavioral models [11] [12]. Prior work reported that, among the various performance evaluation criteria, the NMSE and the WESPR metrics are the strongest candidates for capturing the in-band and out-of-band errors, respectively [9] [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate estimation of memory polynomial model dimension for dynamic nonlinear RF power amplifiers

Hanzala

Sharawi

Hammi

2015

2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)

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In this paper, the dimension estimation of memory polynomial based power amplifiers' behavioral models is investigated. Comprehensive study of state of the art performance assessment metrics is carried out. To ensure the generality of the conclusions, the study is experimentally validated using three power amplifiers prototypes designed using various transistors technologies and amplifier topologies. The results show that the performance assessment metrics are equally reliable for the estimation of the device under test nonlinearity order. However, they are inconsistent when used to estimate its memory depth. This limitation is circumvented by applying memoryless post-compensation technique during the memory depth estimation. The proposed approach demonstrates that the dimension estimation can be made objective and independent of the performance assessment metric.

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