Energy-Efficient Collaborative DRL with Shared Training Data in Low Earth Orbit Satellite Networks 


Vol. 50,  No. 11, pp. 1720-1724, Nov.  2025
10.7840/kics.2025.50.11.1720


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  Abstract

In this study, we propose a cooperative deep reinforcement learning-based frequency reuse coefficient and transmission power control technique to solve the difficulties of resource management due to the fast mobility of low-orbit satellites and limited resources. Specifically, we demonstrate the superiority of the proposed method by comparing and analyzing the no-collaboration method, learning convergence speed, and energy efficiency performance, which defines the Markov decision process (MDP) of the proposed method and applies deep reinforcement learning without cooperation through simulation.

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[IEEE Style]

H. Cho and H. Lee, "Energy-Efficient Collaborative DRL with Shared Training Data in Low Earth Orbit Satellite Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 11, pp. 1720-1724, 2025. DOI: 10.7840/kics.2025.50.11.1720.

[ACM Style]

Hyebin Cho and Howon Lee. 2025. Energy-Efficient Collaborative DRL with Shared Training Data in Low Earth Orbit Satellite Networks. The Journal of Korean Institute of Communications and Information Sciences, 50, 11, (2025), 1720-1724. DOI: 10.7840/kics.2025.50.11.1720.

[KICS Style]

Hyebin Cho and Howon Lee, "Energy-Efficient Collaborative DRL with Shared Training Data in Low Earth Orbit Satellite Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 11, pp. 1720-1724, 11. 2025. (https://doi.org/10.7840/kics.2025.50.11.1720)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880