How to choose a neural network architecture? – A modulation classification example

Warrier, Anand N. and Amuru, SaiDhiraj (2020) How to choose a neural network architecture? – A modulation classification example. In: 2020 IEEE 3rd 5G World Forum, 5GWF 2020 - Conference Proceedings, 10-12 September 2020, Bangalore.

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Abstract

Which neural network architecture should be used for my problem? This is a common question that is encountered nowadays. Having searched a slew of papers that have been published over the last few years in the cross domain of machine learning and wireless communications, the authors found that several researchers working in this multi-disciplinary field continue to have the same question. In this regard, we make an attempt to provide a guide for choosing neural networks using an example application from the field of wireless communications, specifically we consider modulation classification. While deep learning was used to address modulation classification quite extensively using real world data, none of these papers give intuition about the neural network architectures that must be chosen to get good classification performance. During our study and experiments, we realized that this simple example with simple wireless channel models can be used as a reference to understand how to choose the appropriate deep learning models, specifically neural network models, based on the system model for the problem under consideration. In this paper, we provide numerical results to support the intuition that arises for various cases. © 2020 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ARMA; AWGN; Classification; CNN; Communications; Deep Learning; DNN; Fading; LSTM; Modulation; Neural Networks; RNN; Wireless
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Nov 2022 10:37
Last Modified: 23 Nov 2022 10:37
URI: http://raiith.iith.ac.in/id/eprint/11189
Publisher URL: https://doi.org/10.1109/5GWF49715.2020.9221167
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