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


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.

Soft Computing