Inspecting Distortion in the Power Amplifiers with the aid of Neural Networks - Aygun, Sercan; Kouhalvandi, Lida; Gunes, Ece Olcay; Ozoguz, Serdar (IEEE, 2020)
This paper aims to classify the distortion behavior of a power amplifier (PA) with the aid of a neural network. Power amplifiers have quite extensive usage in communication systems especially with the current developments on SG and more. However, distortion in the power amplifiers needs attention to be pre-distorted with the help of a feedback mechanism using direct or indirect methods in the digital domain. In the literature, there are several efforts to understand and reduce distortion in amplifier devices. Therefore, in this paper, the distortion behavior in the power amplifier is inspected using the neural networks. In this work, we have obtained a software-defined network using the strength of the neural network to inspect the distorted and non-distorted data as a binary classification on the actual design of the power amplifier in ill. For this purpose, a neural network system is trained. In the tests, more than 96% accuracy can easily be obtained in an early epoch with the cleverly chosen learning rate (eta) which is optimally outperforming thereabouts after eta = 0.05 till 0.1. Thus, the linearity and non-linearity response of the PA is considered with the help of the trained network.
Keywords: digital pre-distortion; neural networks; power amplifier; software-defined radio; LINEARIZATION; ARCHITECTURE; Computer Science; Telecommunications