Application of Digital Adaptive Pre-distortion to Mobile Wireless Devices

Norris, George B.; Staudinger, Joseph; Chen, Jau-Horng; Rey, Claudio; Pratt, Pete; Sherman, R.; Fraz, H.

doi:10.1109/rfic.2007.380875

Cited by 18 publications

(6 citation statements)

References 5 publications

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“…Hence, an interpolation technique is necessary. Generally, the linear interpolation is widely used in the LUT [10]. The linearly interpolated LUT uses linear interpolation between adjacent LUT entries.…”

Section: Proposed Solutionmentioning

confidence: 99%

Digital predistortion technique based on non‐uniform MP model and interpolated LUT for linearising PAs with memory effects

et al. 2014

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A new digital predistortion technique based on a direct learning architecture is proposed for linearising power amplifiers (PAs) with nonlinear memory effects. It is based on the non-uniform memory polynomial (MP) model and with a quadratically interpolated LUT. There are mainly two innovations. First, compared with the conventional MP model, the non-uniform MP model achieves the same modelling accuracy with less coefficients. Secondly, compared with the linearly interpolated LUT, the quadratically interpolated LUT has higher accuracy, which is adopted to realise a part of the mathematical computations in the predistortion. The proposed solution is validated on a real PA ZFL-2500. It reduces the number of the model's coefficients and achieves a spectrum regrowth suppression of about 15 dB with a table size of only 8.

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Section: Proposed Solutionmentioning

confidence: 99%

Digital predistortion technique based on non‐uniform MP model and interpolated LUT for linearising PAs with memory effects

et al. 2014

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“…Previously explored LUT training procedures in [1] and [2] use feedback and an adaptive algorithm to capture the inverse nonlinearity of the PA. This adds complexity to the transmitter system and also causes convergence and bandwidth issues.…”

Section: B Lut Trainingmentioning

confidence: 99%

Adaptive Predistortion Using a $\Delta\Sigma$ Modulator for Automatic Inversion of Power Amplifier Nonlinearity

Boo

Chung

Dawson

2009

IEEE Trans. Circuits Syst. II

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This brief demonstrates a new adaptive digital predistortion architecture particularly suited to mobile handset applications. The central idea is to build a lookup table (LUT) that directly captures the static compressive nonlinearity of the power amplifier (PA) and then insert this LUT into the feedback path of a ΔΣ modulator. The oversampled ΔΣ modulator automatically performs both the inversion of the PA nonlinearity and the interpolation between LUT entries, permitting complex modulation strategies to be handled with an absolute minimum of LUT entries and with a dramatically simplified computational structure. The advantages of this architecture over previous methods include: 1) there is no need to explicitly invert the PA nonlinearity, reducing the complexity for the system designer; 2) the LUT training is done with an open-loop method, improving the training speed; 3) there is no need to explicitly employ numerical interpolation between LUT entries; and 4) digital-to-analog converter (DAC)nonlinearity is incorporated into the predistortion, allowing fast low-resolution DACs to be used in the final system. We built a proof-of-concept prototype for a 900-MHz, 27-dBm PA transmitting a 16-ary quadrature amplitude modulation (16-QAM) signal with a bandwidth of 3.4 MHz. The predistortion system reduced out-of-band distortion products by 10 dB and improved the error vector magnitude from 3.5% to 2.0%.

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“…The Volterra framework [1], [2] plays an important role in digital pre-distortion (DPD) and baseband modelling of high-power and wideband nonlinear (NL) power amplifiers (PA) [3]- [6], although, in practice, the full-Volterra (FV) model is avoided due to the large number of coefficients to be estimated, the length of the corresponding filter and its on-line running cost. The so-called FV curse of dimensionality has motivated several Volterra pruning techniques in the literature, the most prominent being the memory polynomial (MP) [7], the generalized memory polynomial (GMP) [8], the dynamic deviation reduction (DDR) [9], [10] and block-oriented [11], [12] behavioural models.…”

Section: Introductionmentioning

confidence: 99%

Sparse Flexible Reduced-Volterra Model for Power Amplifier Digital Pre-Distortion

Hemsi

Panazio

2022

IEEE Access

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The Volterra framework plays an important role in digital pre-distortion (DPD) for power amplifier (PA) linearization. In practice, the full-Volterra (FV) model is avoided due to the so-called curse of dimensionality, which has motivated several Volterra pruning techniques in the literature. However, choosing the pruned-Volterra structure with a given complexity that most accurately describes a PA or DPD with unknown characteristics often requires trial and error. The motivation of this article is to propose a reduced-Volterra model that extends traditional memory polynomials, e.g., the generalized memory polynomial (GMP), by including higher-dimensional terms in a flexible and controlled way, avoiding the curse of dimensionality of the Volterra model and resulting in a model that can apply to a wide range of PA and DPD. Furthermore, the article investigates the parsimonious sizing of the proposed model using least absolute shrinkage and selection operator (LASSO) and sparse-group LASSO (SGL) convex optimization, significantly reducing the model's running cost, while preserving its performance. In our experimental validation, the sparse versions of the proposed model achieved 3 dB better adjacent channel power ratio (ACPR) than the conventional GMP model for the same or lower DPD running cost, at the expense of a more costly estimation, which is performed off-line.

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Application of Digital Adaptive Pre-distortion to Mobile Wireless Devices

Cited by 18 publications

References 5 publications

Digital predistortion technique based on non‐uniform MP model and interpolated LUT for linearising PAs with memory effects

Digital predistortion technique based on non‐uniform MP model and interpolated LUT for linearising PAs with memory effects

Adaptive Predistortion Using a $\Delta\Sigma$ Modulator for Automatic Inversion of Power Amplifier Nonlinearity

Sparse Flexible Reduced-Volterra Model for Power Amplifier Digital Pre-Distortion

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