@article{M9BB682FD,
title = "Implementation of Modulation and Channel Coding Recognition Using CNN and Protocol Reverse Engineering Simulation in Blind Communication Environment",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.11.1644",
author = "Hyunwoo Cho, Myoungho Chae, Wansu Lim",
keywords = "modulation recognition, channel coding recognition, Convolutional Neural Network (CNN), protocol reverse engineering, blind communication",
abstract = "This paper describes the implementation of the simulation for modulation and channel coding recognition and protocol reverse engineering using CNN (Convolutional Neural Network) in the blind communication environment where transmitters and receivers do not share communication parameters. The communication channel is assumed to be AWGN channel, and BPSK, QPSK, and 8PSK are used as modulation schemes. For Channel coding, (2, 1, 3), (2, 1, 4), and (2, 1, 5) convolutional codes are used. CNN, a type of deep learning model, is utilized to recognize modulation and channel coding schemes. Additionally, the contiguous sequence pattern algorithm, a protocol revers engineering algorithm, is employed to analyze protocols. The simulation in this paper implements the blind communication environment and can be used as a means to generate data and evaluate the performance of modulation and channel coding recognition and protocol reverse engineering."
}