Digital Predistortion of LTE-A Power Amplifiers Using Compressed-Sampling-Based Unstructured Pruning of Volterra Series

Abdelhafiz, Abubaker; Kwan, Andrew; Hammi, Oualid; Ghannouchi, Fadhel M.

doi:10.1109/tmtt.2014.2360845

Cited by 62 publications

(45 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 MHz. Here, when the model dimension is set to M = 3, ie, the generalized 3-dimensional memory polynomial model (ie, r = 2 and d = 1) is used to represent the PA output and input relations.…”

Section: Simulation Results Using 3-carrier Long-term Evolution Sigmentioning

confidence: 99%

“…Finally, to compare the usefulness and effectiveness of the SGD-based adaptive SP greedy algorithm, the batch mode CS pruning technique called RSAMP 25 is also used for DPD model performance evaluation. Contrary to the batch mode CS pruning techniques, [15][16][17]25 the SGD-based SP greedy algorithm can achieve similar sparsity level adaptively in the unstructured pruning manner, and suitable for adaptive DPD application. According to the results calculated in Table 2, it is worthy of note that the sparse G2DMP model consists of only 60 coefficients and led to the more better NMSE performance in comparison with the other models.…”

Section: Resultsmentioning

confidence: 99%

“…13 The basis pursuit and greedy algorithms are the 2 major algorithmic approaches for sparse signal reconstruction. [15][16][17] However, these reported CS greedy algorithms in their ordinary mode of operation have an inherent batch mode of operation, and hence are not suitable for adaptive DPD schemes. 14 Inherently, the PA systems admit sparse representations; that is, the PA models and DPD models can be characterized by a small number of nonzero parameters.…”

Section: Introductionmentioning

confidence: 99%

“…As one of the efficient greedy algorithms, the novel subspace pursuit (SP) method can provide low computational complexity and high reconstruction accuracy as well as the robustness to cope with inaccurate measurement. [15][16][17] In practice, to properly compensate for the distortion caused by the characteristics change of the PAs in a short time period, the real-time adaptive parameter updating of the DPD must be conducted. Because most of the parameters do not play an important role in PA modeling and DPD, we need to search and select the most significant ones among them.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Yao

Jin

et al. 2017

Circuit Theory & Apps

View full text Add to dashboard Cite

Greedy algorithms in the compressive sensing theory have been formed the essential method for pruning power amplifier (PA) behavioral models and digital predistorters (DPDs). However, the inherent batch mode of these algorithms limits their application in adaptive digital predistortion framework. In this paper, a powerful subspace pursuit greedy scheme combined with stochastic gradient descent adaptive algorithm is proposed to design a class of adaptive sparse DPDs. According to the given sparsity level, the proposed approach can obtain the sparse terms of DPDs and extract the corresponding coefficients adaptively. Performance improvement of the proposed method is validated by simulation results on the adaptive DPD excited by 15-MHz 3-carrier Long-Term Evolution signals and 50-MHz 16 amplitude/phase-shift keying signals. Meanwhile, measurement results on a Doherty PA excited by 30-MHz 3-carrier Long-Term Evolution signals are also performed to verify the advantage of the proposed approach. Simulation and experimental resultsshow that proposed algorithm can efficiently construct the adaptive sparse DPD models with only a small number of parameters; both nonlinear distortions and memory effects in the PA can be almost completely removed. A comparison with the nonsparsity aware DPD techniques and batch mode compressive sensing pruning techniques has been demonstrated that the proposed method exhibit faster convergence, improving tracking capabilities and reduced computational complexity.

show abstract

Section: Simulation Results Using 3-carrier Long-term Evolution Sigmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Yao

Jin

et al. 2017

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…These approaches use to consider structural knowledge of the model to dismiss the terms of a more general full Volterra (FV) series. Unstructured compressed-sensing techniques have been used in [2] and information theory have been taken into account in [3] where the intrinsic sparsity of the Volterra series have been included in the algorithm formulation.…”

Section: Introductionmentioning

confidence: 99%

Structured compressed-sensing for volterra series models

Becerra

Madero‐Ayora

Reina‐Tosina

et al. 2016

2016 IEEE 17th Annual Wireless and Microwave Technology Conference (WAMICON)

View full text Add to dashboard Cite

This paper presents a simple and efficient algorithm for improving the sparse modeling of wireless communications nonlinear devices. This technique consists on a modified greedy algorithm that sorts the regressors of the model by its importance and a maximum likelihood method for the selection of the optimum number of coefficients. This approach has been applied to obtain a model with a reduced number of parameters while upholding its performance capabilities for a commercial power amplifier (PA) driven with an orthogonal frequency division multiplexing signal, following the LTE standard. Results show that the presented algorithm achieves the same level of performance than a reference compressed-sensing algorithm while providing a further reduction in the number of coefficients.

show abstract

A simplified adaptive sparse digital pre‐distorter for joint mitigation of frequency‐dependent transmitter impairments

Yao

Jin

et al. 2019

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

The interplay between the unavoidable various nonlinearities of the direct conversion transmitter, such as local oscillator leakage, power amplifier (PA) nonlinearities, and in-phase and quadrature (I/Q) branch imbalance, and so forth will degrade the communication system performance seriously. To overcome these nonlinear interactive effects, an accurate adaptive sparse behavioral model is proposed for the joint compensation of the transmitter impairments in this article. First, a three-input nonlinear joint compensation model, which is composed of the nonlinear frequency-dependent cross terms between the I and Q branches as well as the magnitudes of the input signal, is developed.Second, to prune the redundant terms and reduce the computational complexity of the full three-input model, an efficient robust quasi-newton-based adaptive greedy algorithm is developed. To verify the performance of the proposed method, the different imperfect transmitters based on single-device GaN Class-F PA and GaN Doherty PA are used for experimental verification and analysis.Experiment results show that the proposed method can efficiently construct a sparse joint compensation model with improved modeling and distortion mitigation capability than the reported I/Q imbalance model, where nonlinear distortion and I/Q imbalance characteristics in the transmitter can be almost completely removed.

show abstract

Digital Predistortion of LTE-A Power Amplifiers Using Compressed-Sampling-Based Unstructured Pruning of Volterra Series

Cited by 62 publications

References 31 publications

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Compressive sensing‐based adaptive sparse predistorter design for power amplifier linearization

Structured compressed-sensing for volterra series models

A simplified adaptive sparse digital pre‐distorter for joint mitigation of frequency‐dependent transmitter impairments

Contact Info

Product

Resources

About