Best Papers
 Handover Minimization Scheme Using Multi-Agent Deep Reinforcement Learning in Multi-Beam Low Earth Orbit Satellites 


Vol. 50,  No. 8, pp. 1196-1206, Aug.  2025
10.7840/kics.2025.50.8.1196


PDF Full-Text
  Abstract

In this paper, we propose a Multi-Agent Proximal Policy Optimization (MAPPO)-based handover strategy for multi-beam Low Earth Orbit (LEO) satellite networks, employing the Centralized Training and Decentralized Execution (CTDE) approach of Multi-Agent Deep Reinforcement Learning (MADRL). The proposed strategy aims to minimize the number of handovers and maximize throughput by considering the cost differences between inter-beam and inter-satellite handovers, user quality of service (QoS) constraints, and load balancing. Each user independently makes handover decisions based on local information (e.g., load and channel conditions within the coverage area), allowing for prompt immediately adaptation to the dynamic and complex environment of multi-beam LEO satellite networks. Simulation results indicate that the proposed algorithm reduces the number of handovers by 39.1% to 75.53% and improves throughput by 14.6% to 157.7% compared to benchmark handover algorithms, thereby objectively demonstrating the superior performance of the proposed approach.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

C. Lee, T. Kim, I. Bang, S. H. Chae, "Handover Minimization Scheme Using Multi-Agent Deep Reinforcement Learning in Multi-Beam Low Earth Orbit Satellites," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 8, pp. 1196-1206, 2025. DOI: 10.7840/kics.2025.50.8.1196.

[ACM Style]

Chungnyeong Lee, Taehoon Kim, Inkyu Bang, and Seong Ho Chae. 2025. Handover Minimization Scheme Using Multi-Agent Deep Reinforcement Learning in Multi-Beam Low Earth Orbit Satellites. The Journal of Korean Institute of Communications and Information Sciences, 50, 8, (2025), 1196-1206. DOI: 10.7840/kics.2025.50.8.1196.

[KICS Style]

Chungnyeong Lee, Taehoon Kim, Inkyu Bang, Seong Ho Chae, "Handover Minimization Scheme Using Multi-Agent Deep Reinforcement Learning in Multi-Beam Low Earth Orbit Satellites," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 8, pp. 1196-1206, 8. 2025. (https://doi.org/10.7840/kics.2025.50.8.1196)
Vol. 50, No. 8 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880