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Hosseinzadeh, Shahram, Jafari Lagran, Ruhollah. (1403). Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid. نشریه هنرهای کاربردی, 3(4), 1-6. doi: 10.22075/mseee.2025.35058.1174
Shahram Hosseinzadeh; Ruhollah Jafari Lagran. "Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid". نشریه هنرهای کاربردی, 3, 4, 1403, 1-6. doi: 10.22075/mseee.2025.35058.1174
Hosseinzadeh, Shahram, Jafari Lagran, Ruhollah. (1403). 'Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid', نشریه هنرهای کاربردی, 3(4), pp. 1-6. doi: 10.22075/mseee.2025.35058.1174
Hosseinzadeh, Shahram, Jafari Lagran, Ruhollah. Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid. نشریه هنرهای کاربردی, 1403; 3(4): 1-6. doi: 10.22075/mseee.2025.35058.1174

Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid

Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 4 - شماره پیاپی 14، اردیبهشت 2024، صفحه 1-6    اصل مقاله (488.31 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.35058.1174
نویسندگان
Shahram Hosseinzadeh* ؛ Ruhollah Jafari Lagran
Azarbaijan Shahid Madani University, Tabriz, Iran.
تاریخ دریافت: 27 مرداد 1403،  تاریخ پذیرش: 17 بهمن 1403 
چکیده
This paper proposes a system for transmitting and receiving encrypted state information via a communication channel in the presence of noise and interference. The proposed system uses a chaotic signal generated by a Lorentz oscillator to encrypt the state information, which is then transmitted through the communication channel. At the receiver end, the received signal is decrypted using a similar key generated by a forced Lorentz oscillator. The accurate determination of the force signal is essential for synchronizing chaotic signals, and this paper proposes the use of reinforcement deep learning agents to train and determine the force signal. The proposed communication scheme involves the use of a state estimator, a master chaotic oscillator, two slave oscillators, and two RL agents. The proposed system was simulated using MATLAB Simulink, and the results show that the errors exhibit a repetitive nature, with low and high values corresponding to the input signal. The proposed system provides a reliable and secure system for transmitting sensitive information over communication channels.
کلیدواژه‌ها
Chaotic Communication System؛ Synchronize- tion؛ Reinforcement Learning
مراجع
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