TY  - JOUR
T1  - Implementation of Modulation and Channel Coding Recognition Using CNN and Protocol Reverse Engineering Simulation in Blind Communication Environment
AU  - Cho, Hyunwoo 
AU  - Chae, Myoungho 
AU  - Lim, Wansu 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.11.1644
KW  - modulation recognition
KW  - channel coding recognition
KW  - Convolutional Neural Network (CNN)
KW  - protocol reverse engineering
KW  - blind communication
AB  - 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.