A digital predistortion technique based on a NARX network to linearize GaN class F power amplifiers

Aguilar-Lobo, Lina María; Garcia-Osorio, A.; Loo‐Yau, J. R.; Ortega-Cisneros, S.; Moreno, P.; Rayas‐Sánchez, José E.; Reynoso-Hernández, J.A.

doi:10.1109/mwscas.2014.6908515

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…An experimental setup to model the inverse characteristics of a PA is used to validate the FPGA implementation of the NARX network [2]. The validation is realized in three stages, in the first stage was realized the measurements of the input and output signals of the PA.…”

Section: Validation and Resultsmentioning

confidence: 99%

“…We are using the NARX network to model a nonlinear system, such as, a power amplifier (PA). However, at this moment only has been development the neural architecture in simulation environment [2]. This work comprises the hardware implementation of the NARX network architecture as a modeled system to obtain the inverse characteristics of a PA.…”

Section: Introductionmentioning

confidence: 99%

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FPGA Implementation of a NARX Network for Modeling Nonlinear Systems

Rentería-Cedano¹,

Aguilar-Lobo²,

Ortega-Cisneros³

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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This paper presents the FPGA implementation of a NARX neural network for the modeling nonlinear systems. The complete neural architecture was implemented with Verilog language in Xilinx ISE Tool with the Virtex-6 FPGA ML605 Evaluation Kit. All operations, such as data processing, weight connections, multipliers, adders and activation function were performed using floating point format, because allows high precision in operations with high complexity. Some resources of Xilinx were used such as multipliers and CORE blocks, and the hyperbolic tangent of the activation is realized based on Taylor series. To validate the implementation results, the NARX network was used to model the inverse characteristics of a power amplifier. The results obtained in the simulation and the FPGA implementation shown a high correspondence.

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Section: Validation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a NARX Network for Modeling Nonlinear Systems

Rentería-Cedano¹,

Aguilar-Lobo²,

Ortega-Cisneros³

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

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show abstract

“…Previous publications have started to explore the use of NARX structures to model or perform DPD for nonlinear PAs. Authors in [7] claimed that Nonlinear Autoregressive with eXogenous inputs can be used to linearize GaN class F PAs using an indirect learning architecture. The proposed three-layer implementation consists of input layer, hidden layer, and output layer.…”

Section: Introductionmentioning

confidence: 99%

A Digital Pre-Distortion Based on Nonlinear Autoregressive With Exogenous Inputs

Varahram

Dooley

Finnerty

et al. 2016

IEEE Microw. Wireless Compon. Lett.

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In this letter, a new pre-distortion technique for power amplifiers in wideband applications is proposed. The proposed pre-distortion technique is based on Nonlinear Autoregressive with Exogenous inputs (NARX). The forward path of the proposed predictive method is based on the memory polynomial. Experimental validation is carried out with 4 carrier WCDMA signal with 20MHz bandwidth and PAPR = 9.8 dB. The results show significant reduction in the number of coefficients with comparable performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) to Volterra series techniques.

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“…The advantage of their recursive method was its greater accuracy with less number of coefficients compared with the conventional methods. In [9], a neural network algorithm to implement digital pre-distortion was used. In [8], a nonlinear autoregressive moving average (NARMA) DPD based on multi-lookup table functions was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…However, a comprehensive analysis was not presented for the approach and a comparison with other methods was not given. In [9], a neural network algorithm to implement digital pre-distortion was used. However, no discussion on stability was provided.…”

Section: Introductionmentioning

confidence: 99%

A low complexity pre‐distortion scheme for power amplifier linearization in wideband applications

Varahram

Dooley

Finnerty

et al. 2016

Int J Communication

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In this paper, a nonlinear autoregressive with exogenous inputs (NARX) digital pre-distortion scheme to linearize power amplifiers is proposed. The proposed NARX digital pre-distortion gives better accuracy and spectral leakage suppression compared with other commonly used Volterra-based techniques. The stability criterion of the NARX digital pre-distortion is derived from the frequency domain analysis. Simulation is carried out with a 20 MHz single carrier long-term evolution signal and two-carrier long-term evolution signal and instantaneous to average ratio of 6.2 dB at 0.01% complementary cumulative distribution function (CCDF). The results of simulation analysis show a slight improvement in adjacent channel leakage ratio performance with 30% reduction in the number of floating point operations compared with conventional predistortion techniques.NMSE, normalized mean square error; MP, memory polynomial; GMP, generalized memory polynomial; NARX DPD, nonlinear autoregressive exogenous digital pre-distortion; LTE, long-term evolution. 8 of 12 P. VARAHRAM ET AL.

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A digital predistortion technique based on a NARX network to linearize GaN class F power amplifiers

Cited by 6 publications

References 7 publications

FPGA Implementation of a NARX Network for Modeling Nonlinear Systems

FPGA Implementation of a NARX Network for Modeling Nonlinear Systems

A Digital Pre-Distortion Based on Nonlinear Autoregressive With Exogenous Inputs

A low complexity pre‐distortion scheme for power amplifier linearization in wideband applications

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