Work

Deep-Learning-Based Automatic Modulation Classification Using Imaging Algorithm

Year: 2021

Type: article

Abstract: This paper presents an automatic modulation classification method that involves the application of various imaging algorithms to a convolutional neural network (CNN). The effect of time-series data imaging on the performance of CNN-based modulation classification is analyzed. Our experiment suggests that converting raw signal data into image data using Markov transition field can reduce the error rate of CNN classification from 34 % to 30 % in case of −6 dB signal to noise ratio (SNR) and from 37 % to 18 % in case of 0 dB SNR. This study shows that time-series imaging is a viable preprocessing method for improving the performance of CNN-based modulation classification.

Source: The Journal of Korean Institute of Electromagnetic Engineering and Science

DOI: 10.5515/kjkiees.2021.32.4.328

Authors: Ji-Yeon Park, Dong-Ho Seo, Hae-Woon Nam

Institutions: Hanyang University, The University of Texas at Austin

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Ministry of Science ICT and Future Planning

Award: G1903186312