Journal
IEEE COMMUNICATIONS LETTERS
Volume 24, Issue 5, Pages 1038-1041Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2020.2970922
Keywords
Modulation; Convolution; Classification algorithms; Computer architecture; Signal to noise ratio; 5G mobile communication; Receivers; Automatic modulation classification; beyond fifth generation (B5G); convolutional neural network (CNN)
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Funding
- National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2018R1A6A1A03024003]
- National Research Foundation of Korea (NRF) - Korea Government (MSIT) [2019R1F1A1064055]
- National Research Foundation of Korea [2019R1F1A1064055] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.
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