Under-sampling effects and computational cost reduction in RF power amplifier behavioral modeling

Wang, Teng; Gilabert, Pere L.; Montoro, Gabriel

doi:10.1109/eumic.2015.7345067

Cited by 10 publications

(11 citation statements)

References 4 publications

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“…In the next section, the BS methods are compared to the histogram-based SSM mesh selecting (MeS) which is an evolved version of the work in [7] with multidimensional IQ mapping to better account for wideband PA memory effects. IV.…”

Section: B Dataset Length Reductionmentioning

confidence: 99%

Dataset Reduction for Neural Network Based Digital Predistorters under Strong Nonlinearities

López‐Bueno

Gilabert

Montoro

2021

2021 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

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The artificial neural networks (ANN) are gaining momentum in the digital predistorters (DPD) thanks to their inherently good approximation capabilities. Under strong or complex power amplifier nonlinearities, the size of the ANN can increase and lead to long training periods which are unaffordable in fast-changing waveform scenarios like those proposed for 5G or 6G. In this work we combine the orthogonal matching pursuit technique together with dataset length reduction methods, to significantly shorten the ANN DPD coefficients update time.

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Section: B Dataset Length Reductionmentioning

confidence: 99%

Dataset Reduction for Neural Network Based Digital Predistorters under Strong Nonlinearities

López‐Bueno

Gilabert

Montoro

2021

2021 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

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“…Assuming that our data matrix has no problems of rank deficiency, we can address the accuracy problem by extending the number of observations (i.e., extending the number of required waveforms to , N shown later in Figure 7) before extracting the DPD coefficients with the LS algorithm, and then by selecting those samples that are more statistically representative of the input domain for each of the observation waveforms. With the so-called mesh-selecting method [16], given a fixed length of data (rows of the data matrix), we can extend the number of observations and, after proper selection of the most statistically representative data, build the data matrix providing robustness to the DPD parameter extraction.…”

Section: E E E P R O O F Waveform Expansion and Mesh-selectingmentioning

confidence: 99%

Amping Up, Saving Power: Digital Predistortion Linearization Strategies for Power Amplifiers Under Wideband 4G\/5G Burst-Like Waveform Operation

et al. 2016

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ourth-generation (4G) communication systems based on orthogonal frequency division multiplexing (OFDM) and the proposed backwards compatible fifthgeneration (5G) variants, like filter-bank multi-carrier (FBMC), are based on modulation techniques that allow significantly increased spectral efficiency and capacity in mobile radio access networks (RANs). However, the use of these modulation techniques impacts the requirements of the radio base stations, which have been traditionally the most energy consuming-element of mobile networks, accounting for up to 80% of the energy consumption of RANs [1]. Non-constant envelope-modulation techniques with high peak-to-average power ratios (PAPRs) require highly linear power amplifier (PA) amplitude and phase responses to fulfill stringent spectral mask and modulation accuracy requirements. This is often achieved with significant PA back-off, which considerably reduces PA efficiency because the PA's maximum efficiency is achieved near the saturation point. The adoption of gallium nitride (GaN) PA technologies and use of digital linearization techniques are playing a key role in building more efficient base

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“…In Reference 7, the authors introduced a method based on signal strength probability density distribution, in which the distribution of the target histogram is the same as the probability density distribution of the original signal. Wang et al proposed a histogram sampling method that considers AM/AM characteristics 8 and a mesh‐selecting (MeS) method, 9 which can be regarded as a two‐dimensional histogram‐based method with full consideration of the probability density distribution of the in‐phase and quadrature channels.…”

Section: Introductionmentioning

confidence: 99%

Digital predistortion based on sample selection with memory effect

Yang

Qiao

et al. 2021

Int J RF Mic Comp-Aid Eng

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In this article, a novel digital predistortion (DPD) method for radio frequency (RF) power amplifiers (PAs) based on adaptive sample selection is proposed. In the traditional approach, the samples are selected randomly based only on the signal magnitude. This kind of selection implies the potential instability problem. To deal with this issue, we propose a new sampling strategy that takes the influence of memory terms into account, achieving better accuracy of normalized mean square error (NMSE) and more stable convergence than state‐of‐the‐art approaches. To further validate our method, a 100‐MHz OFDM signal is used for experiments. Results show that the proposed method trained with just 0.6% of complete data can achieve comparable linearization performance compared with that trained with complete data, and stable convergence is also validated.

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Under-sampling effects and computational cost reduction in RF power amplifier behavioral modeling

Cited by 10 publications

References 4 publications

Dataset Reduction for Neural Network Based Digital Predistorters under Strong Nonlinearities

Dataset Reduction for Neural Network Based Digital Predistorters under Strong Nonlinearities

Amping Up, Saving Power: Digital Predistortion Linearization Strategies for Power Amplifiers Under Wideband 4G\/5G Burst-Like Waveform Operation

Digital predistortion based on sample selection with memory effect

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