Optimal Spacing of a Linearly Interpolated Complex-Gain LUT Predistorter

Ba, S.N.; Waheed, K.; Zhou, G.T.

doi:10.1109/tvt.2009.2034749

Cited by 11 publications

(23 citation statements)

References 17 publications

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“…Several behavioral models and inverse models for nonlinear PAs with memory effects have been proposed in the literature, offering a good inverse model for indirect DPD in most cases [13]- [22]. Among these models, the lookup table (LUT)-based models are generally assumed to be simple to implement; however, optimal spacing and proper bin selection are required for best results [14], [15]. Moreover, when memory effects are considered, a large number of variables need to be stored for cascaded LUTs [16], [17].…”

Section: Pa Behavioral Modeling and Inverse Modeling For Indirectmentioning

confidence: 99%

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Generalized Rational Functions for Reduced-Complexity Behavioral Modeling and Digital Predistortion of Broadband Wireless Transmitters

Rawat

Ghannouchi

et al. 2014

IEEE Trans. Instrum. Meas.

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In this paper, we present and analyze rationalfunction-based digital predistortion (DPD) of transmitters for broadband applications where system noise and prominent memory effects contribute to the overall nonlinearity of the system. The performance is reported for simulation and measured results for gallium nitride (GaN)-based class-AB and laterally diffused MOS (LDMOS)-based Doherty power amplifiers (PAs) using three different wideband code division multiple access signals with peak-to-average-power ratios of around 10 dB. The performance of the proposed model, in terms of normalized meansquare error, adjacent channel power ratio, matrix condition number, and coefficient dispersion, is compared against those of a memory polynomial (MP) model and a previously proposed rational-function-based model. It is shown by simulation and measurement that the previously proposed absolute-term denominator rational functions have limitations in the inverse modeling needed for DPD. A new variation of the rational function is proposed to alleviate this limitation. Depending on the type of PA and signals, a floating-point operation reduction of 8%-38% is reported as compared with a low-complexity MP model.

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Section: Pa Behavioral Modeling and Inverse Modeling For Indirectmentioning

confidence: 99%

“…Moreover, parameter extraction is possible by rewriting (13) in a matrix form y = Aφ DRF−MFOD (14) where y is the output vector with the dimension of L × 1 and L is the length of the training data; and, φ DRF−MFOD is the coefficient vector, which is defined as . .…”

Section: B Rational-function Parameter Extractionmentioning

confidence: 99%

Generalized Rational Functions for Reduced-Complexity Behavioral Modeling and Digital Predistortion of Broadband Wireless Transmitters

Rawat

Ghannouchi

et al. 2014

IEEE Trans. Instrum. Meas.

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“…Linear interpolation greatly reduces the LUT approximation errors and enables significant reduction of the required LUT size [6,29]. If linear interpolation is used, for each feedback sample magnitude |y k | falling between addresses n and n + 1, the interpolated complex-gain is…”

Section: Updating a Linearly-interpolated Lutmentioning

confidence: 99%

“…The spectral regrowth is significantly reduced. The spectral floor using ZOH is 2 to 3 dB higher due to the intrinsic half-bit excess quantization noise of the ZOH as compared to the linear interpolation [29]. Therefore, even when the feedforward predistorter is chosen to be linearly interpolated, the nearest neighbor adaptation can be used in the update branch of the indirect learning architecture, without much performance penalty.…”

Section: Updating a Linearly-interpolated Lutmentioning

confidence: 99%

Efficient Lookup Table-Based Adaptive Baseband Predistortion Architecture for Memoryless Nonlinearity

Waheed

Zhou

2010

EURASIP J. Adv. Signal Process.

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Digital predistortion is an effective means to compensate for the nonlinear effects of a memoryless system. In case of a cellular transmitter, a digital baseband predistorter can mitigate the undesirable nonlinear effects along the signal chain, particularly the nonlinear impairments in the radiofrequency (RF) amplifiers. To be practically feasible, the implementation complexity of the predistorter must be minimized so that it becomes a cost-effective solution for the resource-limited wireless handset. This paper proposes optimizations that facilitate the design of a low-cost high-performance adaptive digital baseband predistorter for memoryless systems. A comparative performance analysis of the amplitude and power lookup table (LUT) indexing schemes is presented. An optimized low-complexity amplitude approximation and its hardware synthesis results are also studied. An efficient LUT predistorter training algorithm that combines the fast convergence speed of the normalized least mean squares (NLMSs) with a small hardware footprint is proposed. Results of fixed-point simulations based on the measured nonlinear characteristics of an RF amplifier are presented.

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“…In this article, we propose a set of new orthonormal basis functions to eliminate the correlation among different monomial terms as well as the correlation among data samples. The proposed orthonormal basis functions can be predetermined and implemented with look up tables (LUTs), which do not increase online computational complexity [15,16]. The fixed-point implementation is feasible and online computational complexity is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

Orthonormal basis functions for memory predistorter in oversampled systems

Yao

Huang

Qian

et al. 2014

Int J RF and Microwave Comp Aid Eng

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Volterra model or memory polynomial model are commonly used to describe the nonlinearity with memory effects for power amplifier (PA) modeling as well as digital predistorter designs. Different monomial terms of the Volterra model or memory polynomial model are highly correlated, which become a challenge during the fixed-point implementation of the coefficients estimation as the data matrix is ill-conditioned. Previous works derived orthogonal basis functions to eliminate the correlation among different monomial terms. Conversely, models of the PAs or the digital predistorters work in the oversampled domain to capture the adjacent band and/or out of band emissions. The correlation among data samples, which was neglected in previous works, can also be a major issue to the numerical instability in the coefficients estimation. In this article, we propose a set of new orthonormal basis functions to eliminate the correlation among different monomial terms as well as the correlation among data samples. As the proposed orthonormal basis functions can be predetermined and implemented with look up tables, the fixed-point implementation is feasible and online computational complexity is greatly reduced. Simulation and experimental results show that the proposed orthonormal basis functions outperform the conventional ones in terms of condition number reduction as well as spectral regrowth suppression.

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Optimal Spacing of a Linearly Interpolated Complex-Gain LUT Predistorter

Cited by 11 publications

References 17 publications

Generalized Rational Functions for Reduced-Complexity Behavioral Modeling and Digital Predistortion of Broadband Wireless Transmitters

Generalized Rational Functions for Reduced-Complexity Behavioral Modeling and Digital Predistortion of Broadband Wireless Transmitters

Efficient Lookup Table-Based Adaptive Baseband Predistortion Architecture for Memoryless Nonlinearity

Orthonormal basis functions for memory predistorter in oversampled systems

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