LD3QN-IR: An Information-Timeliness and Energy Co-Optimization Routing Algorithm for Satellite Networks

YANG Chen; WANG Xinrui; WEI Debin; PAN Chengsheng

doi:10.11959/j.issn.1000-0801.DXKX260438

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LD3QN-IR: An Information-Timeliness and Energy Co-Optimization Routing Algorithm for Satellite Networks

更新时间：2026-09-30

- LD3QN-IR: An Information-Timeliness and Energy Co-Optimization Routing Algorithm for Satellite Networks
- Telecommunications Science (2026)
- 作者机构：
  
  1.大连大学信息工程学院，辽宁大连 116622
  2.南京邮电大学通信与网络技术国家工程研究中心，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61931004)
- DOI：10.11959/j.issn.1000-0801.DXKX260438
  CLC： TP393
- Received：30 June 2026，
  
  Revised：2026-08-13，
  
  Accepted：08 September 2026，
- 稿件说明：
移动端阅览
YANG Chen, WANG Xinrui, WEI Debin, et al. LD3QN-IR: An Information-Timeliness and Energy Co-Optimization Routing Algorithm for Satellite Networks[J/OL]. Telecommunications Science, 2026.
DOI：

YANG Chen, WANG Xinrui, WEI Debin, et al. LD3QN-IR: An Information-Timeliness and Energy Co-Optimization Routing Algorithm for Satellite Networks[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260438.

摘要

针对低轨卫星运动引起的网络拓扑和星间链路状态动态变化，以及业务负载、链路资源和节点能量状态持续演化时，现有卫星网络路由方法难以同时兼顾信息时效性、链路拥塞与节点能量消耗的问题，提出信息时效性与能量协同优化的卫星网络路由算法LD3QN-IR。该算法将网络邻接关系、链路剩余带宽、传输时延、端到端信息年龄及节点剩余能量比例联合构建为状态空间，利用LSTM对历史网络状态进行时序编码，并通过CNN进一步提取状态特征中的局部关联信息；随后采用Dueling Double DQN对状态价值与动作优势进行分解，并利用Double Q机制降低动作价值过估计。在此基础上，通过综合考虑信息年龄、链路利用率、节点能量和路由前进性的奖励函数，引导智能体在信息新鲜度、传输性能和能量消耗之间进行协同权衡。基于STK、NS3与MATLAB的联合动态拓扑仿真结果表明，LD3QN-IR在不同网络负载条件下均获得最高的综合均衡得分，表现出更优的多目标综合性能，能够有效兼顾传输时延、信息新鲜度与节点能量消耗。

Abstract

In low-Earth-orbit (LEO) satellite networks

the network topology and inter-satellite link states were continuously altered by satellite motion

while continuous variations were observed in traffic loads

link resources

and node energy states. Consequently

information timeliness

link congestion

and node energy consumption could not be simultaneously balanced by existing satellite network routing methods. To address this problem

LD3QN-IR

a satellite network routing algorithm for the joint optimization of information timeliness and energy

was proposed. In the proposed algorithm

a state space was constructed by jointly incorporating network adjacency

residual link bandwidth

transmission delay

end-to-end age of information (AoI)

and the residual energy ratio of each node. Historical network states were temporally encoded using a long short-term memory (LSTM) network

and local temporal correlations in the encoded state features were further extracted using a convolutional neural network (CNN). Subsequently

a Dueling Double deep Q-network (Dueling Double DQN) was adopted

through which the action-value function was decomposed into a state-value function and an action-advantage function

while action-value overestimation was reduced using the Double Q mechanism. On this basis

a reward function incorporating AoI

link utilization

residual node energy

and routing progress was designed

through which the agent was guided to balance information freshness

transmission performance

and energy consumption. Joint dynamic-topology simulations were conducted using STK

NS3

and MATLAB. The highest comprehensive balance score was achieved by LD3QN-IR under all tested network loads

and superior multi-objective performance was demonstrated

with end-to-end delay

information freshness

and node energy consumption effectively balanced.

关键词

Keywords

references

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