基于联邦深度强化学习的低轨卫星边缘计算系统计算卸载

贾敏; 吴健; 王欣玉; 郭庆

doi:10.1631/FITEE.2400448

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基于联邦深度强化学习的低轨卫星边缘计算系统计算卸载

常规文章 | Updated：2025-06-09

- 基于联邦深度强化学习的低轨卫星边缘计算系统计算卸载
- Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system
- 信息与电子工程前沿（英文） 2025年26卷第5期页码：805-815
- Affiliations：
  
  School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China
- Author bio：
  
  ‡ Corresponding author
  20B905014@stu.hit.edu.cn
  wang_xinyu@hit.edu.cn
  qguo@hit.edu.cn
- Funds：
  
  National Natural Science Foundation of China(62231012)
- DOI：10.1631/FITEE.2400448
  中图分类号： TN929.5
- 收稿日期：2024-05-28，
  
  修回日期：2024-10-15，
  
  网络出版日期：2024-12-27，
  
  纸质出版日期：2025-05
- Accepted：
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贾敏, 吴健, 王欣玉, 等. 基于联邦深度强化学习的低轨卫星边缘计算系统计算卸载[J]. 信息与电子工程前沿（英文）, 2025,26(5):805-815.

Min JIA, Jian WU, Xinyu WANG, et al. Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 805-815.
贾敏, 吴健, 王欣玉, 等. 基于联邦深度强化学习的低轨卫星边缘计算系统计算卸载[J]. 信息与电子工程前沿（英文）, 2025,26(5):805-815. DOI： 10.1631/FITEE.2400448.

Min JIA, Jian WU, Xinyu WANG, et al. Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 805-815. DOI： 10.1631/FITEE.2400448.

摘要

最近研究表明系统容量对蜂窝网络非常重要。本文考虑最大化蜂窝网络下行链路和上行链路的加权和速率，其中每个小区由一个全双工基站和半双工用户组成。联邦学习可以在没有集中数据的情况下训练模型，实现对用户数据的隐私保护。将移动边缘计算服务器放置在低轨卫星上，可形成低轨卫星边缘计算系统，大大提高卫星的处理能力。因此，本文将联邦学习和移动边缘计算结合，提出一种基于联邦学习的计算卸载算法，在保证用户数据安全的同时最大化加权和速率。采用具有出色全局搜索能力的深度强化学习算法解决子信道分配和功率分配问题。仿真结果表明，与基准算法相比，该算法实现了最大的加权和速率，并具有良好收敛性能。

Abstract

Recent studies have shown that system capacity is very important for cellular networks. In this paper

we consider maximizing the weighted sum-rate of the cellular network downlink and uplink

where each cell consists of a full-duplex (FD) base station (BS) and half-duplex (HD) users. Federated learning (FL) can train models in the absence of centralized data

which can achieve privacy protection of user data. A low Earth orbit (LEO) satellite edge computing system (LSECS) can be formed by placing the mobile edge computing (MEC) servers on LEO satellites

which greatly increases the processing capacities of the satellites. Therefore

we consider a combination of FL and MEC and propose an FL-based computation offloading algorithm to maximize the weighted sum-rate while ensuring the security of user data. We consider solving the sub-channel assignment and power allocation problems using deep reinforcement learning (DRL) algorithms with excellent global search capabilities. The simulation results show that our proposed algorithm achieves the maximum weighted sum-rate compared with the baseline algorithms and excellent convergence.

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references

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