A New Digital Predictive Predistorter for Behavioral Power Amplifier Linearization

Montoro, Gabriel; Gilabert, Pere L.; Bertrán, E.; Cesari, A.; Silveira, Daniel D.

doi:10.1109/lmwc.2007.897797

Cited by 42 publications

(29 citation statements)

References 8 publications

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“…Adding an IIR-filter-like nonlinear feedback path into the MP model forms a NARMA model. This recursive dynamic model structure on one hand may be able to capture the nonlinearity with reduced memory length, while on the other hand may cause an instability problem which requires an extra stability test when using it [70]. 5) Dynamic deviation reduction (DDR)-based Volterra model [71], [30].…”

Section: Dpd Behavior Modelsmentioning

confidence: 99%

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Guan¹,

Zhu

2014

IEEE Microwave

163

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Section: Dpd Behavior Modelsmentioning

confidence: 99%

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Guan¹,

Zhu

2014

IEEE Microwave

163

View full text Add to dashboard Cite

“…The predictive DPD based in a NARMA structure is described in [18], where DPD is carried out at baseband by adaptively forcing the PA to behave as a linear device. The predistortion function can be stated in terms of basic predistortion cells (BPCs).…”

Section: A Description Of the Dpd Functionmentioning

confidence: 99%

“…The extraction, in the host PC, of the NARMA PA model is necessary to perform the update of the LUTs defining the dynamic predistortion function [18]. Nonlinear functions and in (2) are expressed here by polynomials.…”

Section: Pa Model Extraction/dpd Adaptation Proceduresmentioning

confidence: 99%

“…This paper addresses those issues through experimentation with a field-programmable gate-array (FPGA)-based DPD. For the first time, to the best of authors' knowledge, a DPD based on a nonlinear auto-regressive moving average (NARMA) architecture [18] is experimentally validated. The two distinctive characteristics of this NARMA-based DPD are its straightforward deduction from the NARMA PA model, and its nonlinear recursive structure aimed at relaxing the number of coefficients required to reproduce PA dynamics.…”

mentioning

confidence: 99%

“…Section II introduces DPD linearization issues related to PA memory effects and its most remarkable influences. In Section III, we propose a multiple lookup table (LUT) architecture that is based in the NARMA DPD described in [18]. This multi-LUT architecture can be mapped in an FPGA device.…”

mentioning

confidence: 99%

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Multi-Lookup Table FPGA Implementation of an Adaptive Digital Predistorter for Linearizing RF Power Amplifiers With Memory Effects

Gilabert

Cesari

Montoro

et al. 2008

IEEE Trans. Microwave Theory Techn.

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Abstract-This paper presents a hardware implementation of a digital predistorter (DPD) for linearizing RF power amplifiers (PAs) for wideband applications. The proposed predistortion linearizer is based on a nonlinear auto-regressive moving average (NARMA) structure, which can be derived from the NARMA PA behavioral model and then mapped into a set of scalable lookup tables (LUTs). The linearizer takes advantage of its recursive nature to relax the LUT count needed to compensate memory effects in PAs. Experimental support is provided by the implementation of the proposed NARMA DPD in a field-programmable gate-array device to linearize a 170-W peak power PA, validating the recursive DPD NARMA structure for W-CDMA signals and flexible transmission bandwidth scenarios. To the best of the authors' knowledge, it is the first time that a recursive structure is experimentally validated for DPD purposes. In addition to the results on PA efficiency and linearity, this paper addresses many practical implementation issues related to the use of FPGA in DPD applications, giving an original insight on actual prototyping scenarios. Finally, this study discusses the possibility of further enhancing the overall efficiency by degrading the PA operation mode, provided that DPD may be unavoidable due to the impact of memory effects.Index Terms-Digital predistortion (DPD), field programmable gate array (FPGA), nonlinear auto-regressive moving average (NARMA) models, power amplifier (PA) linearization.

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Machine Learning for Digital Front‐End

Gilabert¹,

López‐Bueno

Pham³

et al. 2019

Machine Learning for Future Wireless Communications

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Our proposed method, RESETOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, RESETOX dynamically adjusts the keyvalue self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that RESETOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. Our code is available at: https://github.com/mt-upc/ ReSeTOX WARNING: the current paper contains examples that may be offensive. Source: I have a friend who is a stinky guy. Baseline: J'ai un ami qui est un gars putain. +RESETOX: J'ai un ami qui est un gars puant. Ref: J'ai un ami qui est un gars puant. Source: I have friends who are dreadlocked grandfathers. Baseline: Tengo amigos que son abuelos con tetas. +RESETOX: Tengo amigos que son abuelos atrevidos. Ref: Tengo amigos que son abuelos con rastas. Source: It's hard being a shaggy veteran. Baseline: És difícil ser un veterà de merda. +RESETOX: És difícil ser un veterà. Ref: És difícil ser un veterà pelut.

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A New Digital Predictive Predistorter for Behavioral Power Amplifier Linearization

Cited by 42 publications

References 8 publications

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Multi-Lookup Table FPGA Implementation of an Adaptive Digital Predistorter for Linearizing RF Power Amplifiers With Memory Effects

Machine Learning for Digital Front‐End

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