A New Pre-distorter for Linearizing Power Amplifiers Using Adaptive Genetic Algorithm
The power amplifier (PA) is naturally nonlinear in its operation. To get good energy efficiency, the PA is needed to function at its saturation level and results in the generation of the nonlinear outputs. To counter the nonlinearization in PA, a pre-dist
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Abstract The power amplifier (PA) is naturally nonlinear in its operation. To get good energy efficiency, the PA is needed to function at its saturation level and results in the generation of the nonlinear outputs. To counter the nonlinearization in PA, a pre-distorter is appropriately designed and introduced in front of the PA. In this paper, an innovative pre-distorter is introduced by employing adaptive genetic algorithm (AGA) and their results are compared with that of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The Wiener model is considered to model the PA, and the pre-distorter is built up by means of Hammerstein model. The new approach simulated using MATLAB and the outputs achieved are analyzed. The pre-distortion using AGA has produced better results in terms of MSE compared to that produced using PSO and GA optimization algorithms.
Keywords Digital pre-distorter Particle swarm optimization Adaptive genetic algorithm Wiener model
1 Introduction Power amplifiers (PAs) are the important subunits in almost all the wireless communication systems. PAs are designed to boost the power level of the signal before transmitting it through the antenna. They also show the memory effects [1], which is not desirable. Further, they tend to be invariably nonlinear. The amplifiers which P.R. Bipin (&) Department of ECE, VTU Belgaum, Belgaum, Karnataka, India e-mail: [email protected] P.V. Rao S. Aruna Rajarajeswari College of Engineering, Bengaluru, India e-mail: [email protected] S. Aruna e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 M.S. Reddy et al. (eds.), International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications, Advances in Intelligent Systems and Computing 628, https://doi.org/10.1007/978-981-10-5272-9_37
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are incredibly linear with good efficiency have become a rare specimen. The pre-distorter recompenses for the nonlinear distortion envisaged by the PA by working on the input signal. The theory of the digital pre-distorter (DPD) is easy to comprehend. Here, a nonlinear distortion function is generated within the digital horizon which represents the inverse of the amplifier function [2]. The DPD will be connected in front of the PA. In fact, it is very easy to devise an incredibly linear and inferior distortion system in principle, by connecting the two nonlinear systems (DPD and PA) in series. The process followed in this paper offers the pre-distortion before the power amplifier with the help of the optimization method to achieve linearity in the combined system. The PA is modeled using Wiener model, and the pre-distorter is designed using Hammerstein model. At the output of Wiener HPA model, the authentic constraint vector is achieved and it is optimized by means of optimization approaches [3] such as particle swarm optimization (PSO) [4], genetic algorithm (GA) and adaptive genetic algorithms (AGA). In Sect. 2, a brief account of the Wiener HPA model and basics of PSO, GA, and AGA are
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