Orthonormal basis functions for memory predistorter in oversampled systems

Yao, Saijie; Huang, Hao; Qian, Hua; Feng, Weifeng

doi:10.1002/mmce.20850

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…We compare the RV 2 LASSO and RV 2 SGL run for two sets of sparsity parameters, the one that corresponds to the best AIC scores and another that implies a more reduced model complexity. The RV 2 LASSO and RV 2 SGL models outperform the GMP and CRV models, as well as the sparse selection of regressors based on the greedy orthogonal matching pursuit (OMP) [34], [35], [67], as in the tables. The LASSO sparse estimation technique preserves the model's performance, while significantly reducing its running cost.…”

Section: ) Rv2 Lasso Resultsmentioning

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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Section: ) Rv2 Lasso Resultsmentioning

confidence: 99%

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

Hemsi

Panazio

2022

IEEE Access

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“…In recent years, the digital predistortion (DPD) has been a very popular and an active linearization technique for radio frequency PAs . Before linearizing with DPD, an accurate behavioral model for PAs is necessary.…”

Section: Introductionmentioning

confidence: 99%

Hybrid harmonic‐intermodulation distortion behavioral model for shortwave power amplifiers

Liu

Yan

et al. 2019

Int J RF Microw Comput Aided Eng

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In this article, a hybrid harmonic-intermodulation distortion (HHIMD) behavioral model is proposed for imitating the shortwave power amplifier (SWPA) behavioral distortion, that is, harmonic distortion, intermodulation distortion, and memory effect. The proposed model is composed of an intermodulation distortion module, a quadrature digital up-converter module and a harmonic distortion module. To demonstrate the modeling accuracy, the proposed model and measured SWPA are compared in the time domain and spectrum domain. It is shown that the proposed model can imitate the SWPA not only harmonic and intermodulation distortion, but also memory effect. The modeling normalized mean squared error between the HHIMD and measured SWPA output is lower than −44-dB.behavioral modeling, harmonic distortion, shortwave power amplifier

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“…The high dimensionality of the Volterra series model has motivated sparse, parsimonious approximate solutions (STANKOVIC et al, 2018), (BRAITHWAITE, 2017), also used in the context of compressed sensing (CS), to the original model. The more prominent ones have been applied to PA and DPD, such as the least absolute shrinkage and selection operator (LASSO) (ZENTENO et al, 2015), (KEKATOS;GIANNAKIS, 2011), (WISELL;JALDEN;, ridge regression (GUAN; ZHU, 2012) and orthogonal matching pursuit (OMP) (ABDELHAFIZ et al, 2014), (TOSINA et al, 2015), (YAO et al, 2014). In this thesis, firstly, the LASSO technique is applied to the WHFB DPD model in chapter 6 and to pruned-Volterra models in chapter 7.…”

Section: Pa Digital Pre-distortionmentioning

confidence: 99%

Digital signal processing techniques for the compensation of analog circuit impairments in impedance mismatched radio-frequency transmitters.

Hemsi¹

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Esta tese investiga e propõe novos modelos para a compensação digital de imperfeições de rádio frequência (RFradio frequency) em transmissores sem fio de banda larga, mais especificamente para amplificadores de potência (PApower amplifiers) não-lineares e sujeitos a descasamento de impedância de carga (LMMload mismatch). Tais imperfeições em transmissores de RF, juntamente com o desbalanceamento entre as componentes em fase e quadratura (IQin-phase and quadrature) em moduladores digitais, são responsáveis por degradar o desempenho do transmissor, tanto em termos de pureza espectral, quanto em qualidade da modulação e taxa de erro de bit (BERbit error rate).Diversos cenários práticos em que PAs estão sujeitos a LMM motivam a pesquisa por modelos comportamentais não-lineares e com memória voltados a predistorção digital (DPDdigital pre-distortion) mais avançados, capazes de superar as limitações relatadas na literatura de modelos tradicionais e que sejam menos complexos do que as abordagens existentes para PA LMM.Nesta tese é proposta a aplicação do modelo polinomial Wiener-Hammerstein com realimentação (WHFB -Wiener-Hammerstein with feedback) como modelo comportamental simplificado para DPD no contexto de PAs sujeitos a LMM.A elevada dimensionalidade da estrutura WHFB proposta pode ser reduzida através de técnicas esparsas de estimação, tais como o operador de encolhimento e seleção absoluto mínimo (LASSOleast absolute shrinkage and selection operator) e extensões de LASSO em blocos, que são capazes de diminuir significativamente o número de coeficientes necessário, reduzindo, assim, o comprimento do filtro de DPD e, proporcionalmente, o custo da filtragem.Além disso, extensões de LASSO voltadas a blocos, como Group-LASSO e Sparse-group LASSO, são aplicadas no contexto de dimensionamento do modelo, ou seja, na tarefa de determinar valores adequados para seus parâmetros, o que, tradicionalmente, requer uma busca exaustiva.Os modelos WHFB denso e esparso são validados experimentalmente através de medições em um set-up de testes experimental e também comparados a outros, incluindo modelos baseados na decomposição em fatores paralelos (PARAFACparallel factors) e na expansão de Laguerre, demonstrando, assim, sua capacidade de compensar adequadamente PAs sujeitos a LMM.Finalmente, é proposta uma nova estratégia de redução do modelo de Volterra, que resulta em um polinômio de memória mais flexível e modular, em que os parâmetros são escolhidos independentemente para cada ordem de não linearidade. Esta abordagem flexível é capaz de descrever com precisão uma gama ampla de condições operacionais/ ambientais de PAs.

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Orthonormal basis functions for memory predistorter in oversampled systems

Cited by 5 publications

References 23 publications

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

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

Hybrid harmonic‐intermodulation distortion behavioral model for shortwave power amplifiers

Digital signal processing techniques for the compensation of analog circuit impairments in impedance mismatched radio-frequency transmitters.

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