Piecewise Pre-Equalized Linearization of the Wireless Transmitter With a Doherty Amplifier

Kim, Wan-Jong; Cho, Kyoung-Joon; Stapleton, S.P.; Kim, Jong‐Heon

doi:10.1109/tmtt.2006.880639

Cited by 36 publications

(15 citation statements)

References 17 publications

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“…For example, piecewising the basis function of the model can generate certain modified models with better performance such as the piecewise polynomial model [82], the vector threshold decomposition based Volterra model [83], and vector-switched model proposed in [84], which actually utilize different characterization parameters (local parameters) for different signal magnitude/vector ranges;…”

Section: Summary Of Dpd Modelsmentioning

confidence: 99%

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Guan¹,

Zhu

2014

IEEE Microwave

180

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Section: Summary Of Dpd Modelsmentioning

confidence: 99%

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

Guan¹,

Zhu

2014

IEEE Microwave

180

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“…This issue may not be of concern when laboratory setups are used for delayed offline DPD [4]- [7], [33], where large acquisition capabilities may allow a one-step reliable estimation without the need of recursion.…”

Section: Adaptation Proceduresmentioning

confidence: 99%

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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“…3) The third step consists in inverting the nonlinear function since it is necessary to find the digital PD output as it is shown in (4). 4) The last step consists in generating all necessary LUTs contents to implement the predistorter function described in (4). And then back to step 1.…”

Section: Predictive Predistortion System Descriptionmentioning

confidence: 99%

“…Full Volterra series and Neural Networks are typically limited to PA behavioral modeling, because of their computational complexity in a digital PD application. The most usual models employed for digital PD are: memory polynomials or nonlinear moving average (MA) models [2], [3], Hammerstein [4], [5], and Wiener [6], [7] models with their cascade and parallel variants and using finite or infinite impulse response (FIR or IIR) filters to model the linear time invariant block.…”

mentioning

confidence: 99%

“…The estimation of the predistorter function is commonly made by using the indirect learning approach or postdistortion and translation method (e.g., [3], [4], and [8]). However, this method assumes the commutative property for cascading nonlinear systems, which is not rigorous at all, despite the good results shown by this method regarding linearity improvement.…”

mentioning

confidence: 99%

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A New Digital Predictive Predistorter for Behavioral Power Amplifier Linearization

Montoro

Gilabert

Bertrán

et al. 2007

IEEE Microw. Wireless Compon. Lett.

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Abstract-This letter presents a new digital adaptive predistorter (PD) for power amplifier (PA) linearization based on a nonlinear auto-regressive moving average (NARMA) structure. The distinctive characteristic of this PD is its straightforward deduction from the NARMA PA model, without the need of using an indirect learning approach to identify the PD function. The PD itself presents a NARMA structure, and hence it can be quickly implemented by means of lookup tables. Single and multicarrier modulated signals collected from a three-stage LDMOS class AB PA, with a maximum output power of 48-dBm CW have been used to validate the linearity performance of this new predictive predistorter.Index Terms-Amplifier distoriton, digital predistorter (PD), digital radio, direct learning approach, linearization, microwave power amplifiers (PAs), nonlinear auto-regressive moving average (NARMA), radio transmitters.

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Piecewise Pre-Equalized Linearization of the Wireless Transmitter With a Doherty Amplifier

Cited by 36 publications

References 17 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

A New Digital Predictive Predistorter for Behavioral Power Amplifier Linearization

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