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Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation

Abstract

Accurate modeling of power amplifiers (PA) is of upmost importance in the design process of wireless communication systems where a high linearity and efficiency is required. To deal with the nonlinear behavior of PAs effectively a linearization stage is applied to minimize the distortions of in-band and adjacent transmission channels, which translate to an improvement of the signal integrity and the operation cost of the transmitter system. This paper presents a method based on genetic programming with a local search heuristic (GP-LS) to emulate the electrical memory effects by using the characteristic conversion curves of the radio frequency (RF) PA NXP Semiconductor of 10 W GaN HEMT working at 2.34 GHz. This method is compared with an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) through several performance metrics (NMSE, MAE and correlation coefficient), with GP-LS achieving a better modeling accuracy. Moreover, the models produced by GP-LS permit a reduction in the required hardware resources, when it is implemented on a Field-Programmable Gate Array through the DSP Builder tool. The models are derived using a data-driven approach, posed in two different ways. Firstly, experiments are performed using a testbed Arria V GX for a flexible vector signal generation that provides the raw data of the PA characterization using an LTE-Advanced signal with 10-MHz bandwidth. Secondly, the modeling is derived from a filtered version of the data and then adding a high-frequency signal as a post processing step to approximate the true behavior of the system. In both cases, the models are generated with ANFIS and GP-LS, performing extensive logic-based simulations and implementing the models on a Cyclone III development board. Both approaches are compared based on accuracy and required hardware resources, with GP-LS substantially outperforming ANFIS. These results suggest that the GP-LS models can be implemented in a digital predistortion chain and used in the linearization stage for a RF-PA.

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Acknowledgements

The authors would like to express their gratitude to the IPN for its financial support by the Project “SIP-20170588”. Funding for this work was also provided by CONACYT (Mexico) Basic Science Research Project No. 178323, the FP7-Marie Curie-IRSES 2013 European Commission program through project ACoBSEC with contract No. 612689, and CONACYT Project FC-2015-2/944 “Aprendizaje evolutivo a gran escala”. Finally, first and fourth author were, respectively, supported by CONACYT scholarships Nos. 385469 and 294213.

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Correspondence to José Ricardo Cárdenas-Valdez.

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Communicated by V. Loia.

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Galaviz-Aguilar, J.A., Roblin, P., Cárdenas-Valdez, J.R. et al. Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation. Soft Comput 23, 2463–2481 (2019). https://doi.org/10.1007/s00500-017-2941-8

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  • DOI: https://doi.org/10.1007/s00500-017-2941-8

Keywords

  • ANFIS
  • Digital predistortion
  • Genetic programming
  • Linearization
  • Power amplifier modeling
  • Radio frequency