Accurate and low complexity polynomial and neural network models for PA digital pre-distortion

Khawam, Yahya; Albasha, Lutfi; Mir, Hasan

doi:10.1109/mms.2016.7803788

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Each model has a different structure, complexity (number of model parameters) and error performance. Digital Pre-distorters (DPD) and power amplifiers (PA) can be modelled using memory polynomials [5]- [7] as well as neural networks (NN) [8]- [16]. Real-valued and complex-valued NNs are developed and published in the literature.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

et al. 2020

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In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as 45.3 − dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as 46.1 − dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.

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Section: Introductionmentioning

confidence: 99%

Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

et al. 2020

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Orthogonal Scalar Feedback Digital Pre-Distortion Linearization

Rodrigues

Pimenta

Souza

et al. 2018

IEEE Trans. on Broadcast.

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Deep Learning Based Receivers for IEEE 802.11p Standard with High Power Amplifiers Distortions

Reis

Medjahdi

Brante

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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In recent years, vehicular communication has attracted significant research attention for its potential as a fifth generation (5G) application. The IEEE 802.11p standard enables the wireless technology that defines vehicular communication and, due to the time-varying characteristic, one of its critical challenges is to ensure communication reliability. Moreover, this standard is based on orthogonal frequency division multiplexing (OFDM) transmission scheme, which may suffer from nonlinear distortions induced by high power amplifiers (HPA) at the transmitter, degrading the channel estimation and detection performance of the receivers. In this work, the application of deep learning (DL) based channel estimation schemes to the IEEE 802.11p standard in presence of HPA distortions is presented. Simulation results show that the deep neural network (DNN) based estimation schemes outperform conventional data-pilot aided (DPA) and spectral temporal averaging (STA) estimators in terms of bit error rate and normalized mean squared error, evidencing their superiority in providing reliable estimation in mobility scenarios in presence of HPA nonlinear distortions. Furthermore, the hybrid solution, employing a joint DNNbased post-processing and conventional estimation, achieves less computational complexity than the DL-based proposal on top of the initial DPA estimation.

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Accurate and low complexity polynomial and neural network models for PA digital pre-distortion

Cited by 3 publications

References 11 publications

Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

Orthogonal Scalar Feedback Digital Pre-Distortion Linearization

Deep Learning Based Receivers for IEEE 802.11p Standard with High Power Amplifiers Distortions

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