TY - JOUR
T1 - Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation
AU - Galaviz-Aguilar, José Alejandro
AU - Roblin, Patrick
AU - Cárdenas-Valdez, José Ricardo
AU - Z-Flores, Emigdio
AU - Trujillo, Leonardo
AU - Nuñez-Pérez, José Cruz
AU - Schütze, Oliver
N1 - Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2019/4/15
Y1 - 2019/4/15
N2 - 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.
AB - 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.
KW - ANFIS
KW - Digital predistortion
KW - Genetic programming
KW - Linearization
KW - Power amplifier modeling
KW - Radio frequency
UR - http://www.scopus.com/inward/record.url?scp=85034854430&partnerID=8YFLogxK
U2 - 10.1007/s00500-017-2941-8
DO - 10.1007/s00500-017-2941-8
M3 - Artículo
SN - 1432-7643
VL - 23
SP - 2463
EP - 2481
JO - Soft Computing
JF - Soft Computing
IS - 7
ER -