Inspecting Distortion in the Power Amplifiers with the aid of Neural Networks
| Autor: | Sercan Aygun, Lida Kouhalvandi, Ece Olcay Gunes, Serdar Ozoguz |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science Amplifier Distortion 0202 electrical engineering electronic engineering information engineering Electronic engineering Linearity 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Software-defined radio Communications system Power (physics) |
| Zdroj: | 2020 22nd International Conference on Advanced Communication Technology (ICACT). |
| DOI: | 10.23919/icact48636.2020.9061564 |
| Popis: | 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 5G 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 [1]. 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 (ŋ) which is optimally outperforming thereabouts after ŋ=0.05 till 0.1. Thus, the linearity and non-linearity response of the PA is considered with the help of the trained network. |
| Databáze: | OpenAIRE |
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