In this paper, 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 accuracy rate and computing time.
Congratulations Mr. Ade Pitra Hermawan!
Your article "CNN-Based Automatic Modulation Classification for Beyond 5G Communications", has just been published on IEEE Xplore.
Journal: IEEE Communications Letters
Issue Date: MAY 2020
Volume: 24, Issue:5
On Page(s): 1038-1041
Print ISSN: 1089-7798
Online ISSN: 1558-2558
Digital Object Identifier: 10.1109/LCOMM.2020.2970922