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

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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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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).

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