An Intelligent Computing Resource Deployment Method for Mega Satellite Constellations

HAO Qi; NIU Qingqing; CAO Xinyue; LI Xiaohan; DU Ping

doi:10.11999/JEIT260408

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HAO Qi, NIU Qingqing, CAO Xinyue, LI Xiaohan, DU Ping. An Intelligent Computing Resource Deployment Method for Mega Satellite Constellations[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260408

Citation:

HAO Qi, NIU Qingqing, CAO Xinyue, LI Xiaohan, DU Ping. An Intelligent Computing Resource Deployment Method for Mega Satellite Constellations[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260408

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An Intelligent Computing Resource Deployment Method for Mega Satellite Constellations

doi: 10.11999/JEIT260408 cstr: 32379.14.JEIT260408

1.
Shanghai Satellite Network Research Institute Co. Ltd., Shanghai 200120, China
2.
State Key Laboratory of Satellite Network, Shanghai 200120, China
3.
Shanghai Key Laboratory of Satellite Network, Shanghai 200120, China

Funds: The National Science and Technology Major Project (2026ZD1307500)

Received Date: 2026-04-08
Accepted Date: 2026-10-19
Rev Recd Date: 2026-10-18

Available Online: 2026-10-22

Abstract

Abstract

Objective With the continued expansion of satellite constellations and the emergence of new services, satellite computing is expected to substantially improve the service capabilities of large-scale satellite networks. However, limited onboard computing resources make it challenging to determine how many computing nodes to deploy and where to place them. In highly dynamic satellite networks, topology changes affect satellite connectivity, computing-resource distribution, service coverage, and the costs associated with deployment and reconfiguration. An effective deployment strategy therefore requires a graph-based model that captures these coupled effects. To address this problem, a method combining a Graph Neural Network (GNN) and Proximal Policy Optimization (PPO), termed GNN-PPO, is proposed. The GNN encodes neighborhood information to represent inter-satellite connectivity and the spatial distribution of computing resources. Based on this representation, the method determines the number of computing nodes to deploy and selects their locations, providing a basis for computing-resource deployment in future satellite constellations. Methods The GNN-PPO framework uses the GNN to encode the dynamic states of satellite nodes and Inter-Satellite Links (ISLs). The input state includes satellite topology, available computing resources, task-related computing demand, node energy and load, candidate-satellite status, computing-node deployment status, and the ratio of deployed computing nodes to the total number of satellites. Link features include bandwidth and link availability. Through message passing, the GNN aggregates neighborhood topology and resource information into a fixed-length graph representation that serves as input to the PPO agent. The agent uses hierarchical actions: the upper-level action determines whether to increase, decrease, or maintain the number of computing nodes, whereas the lower-level action determines the magnitude of the change. After the target number of computing nodes is determined, a heuristic procedure selects their deployment locations based on candidate-satellite degree, remaining computing resources, and degree centrality. The reward function combines a hop-count penalty, energy penalties for reconfiguration and transmission, a computing-node-count penalty, and positive rewards for computing-service coverage and coverage efficiency. This design enables the agent to balance service performance against deployment and reconfiguration costs. Results and Discussions The proposed GNN-PPO method converges more effectively than the standalone PPO, Deep Q-Network (DQN), and Advantage Actor-Critic (A2C) baselines (Fig. 3). At a network scale of 700 satellites, the method reduces the number of deployed computing nodes by approximately 29% compared with Particle Swarm Optimization (PSO) and lowers reconfiguration energy consumption by approximately 35.8% compared with DQN (Fig. 5). Across the tested hop constraints (h=1, 2, and 3) and Computing Structure Graph (CSG) types, the average management hop count ranges from approximately 2.5 to 3.3 hops, while the average reconfiguration energy consumption ranges from 0.9 to 1.2 J (Table 4). The average transmission energy consumption of the tree CSG is approximately 15 times that of the star and fully connected CSGs. These results indicate that star and fully connected CSGs offer more favorable trade-offs between deployment costs and management performance in the simulated large-scale satellite networks. Conclusions The proposed GNN-PPO method converges more effectively than the standalone PPO, Deep Q-Network (DQN), and Advantage Actor-Critic (A2C) baselines (Fig. 3). At a network scale of 700 satellites, the method reduces the number of deployed computing nodes by approximately 29% compared with Particle Swarm Optimization (PSO) and lowers reconfiguration energy consumption by approximately 35.8% compared with DQN (Fig. 5). Across the tested hop constraints (h=1, 2, and 3) and Computing Structure Graph (CSG) types, the average management hop count ranges from approximately 2.5 to 3.3 hops, while the average reconfiguration energy consumption ranges from 0.9 to 1.2 J (Table 4). The average transmission energy consumption of the tree CSG is approximately 15 times that of the star and fully connected CSGs. These results indicate that star and fully connected CSGs offer more favorable trade-offs between deployment costs and management performance in the simulated large-scale satellite networks
- Mega satellite constellations,
- Reinforcement learning,
- Computing resources,
- Intelligent deployment

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References(21)

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