Aug 2026· 2026 IEEE/CIC International Conference on Communications in China (ICCC)· pp. 1383-1388· 0 citations· 14 references
Abstract
To address the challenges of multi-service congestion and load imbalance in Low Earth Orbit (LEO) networks, stemming from highly dynamic spatio-temporal characteristics and constrained link capacities, this paper proposes a joint optimization method for routing and load balancing based on Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL). The proposed method constructs a heterogeneous graph model to provide a unified representation of the complex dependencies between service requirements and physical topology, while modeling multi-service scheduling as a Markov Decision Process (MDP). Building upon this, a dual-head Actor-Critic architecture based on Proximal Policy Optimization (PPO) is designed to facilitate end-to-end policy learning. Simulation results demonstrate that, compared to traditional algorithms, the proposed method significantly enhances service success rates and reduces link load variance across varying traffic intensities, effectively achieving efficient utilization and balanced allocation of network resources.
Efficient radio resource allocation is pivotal for maximizing the service capability of Low Earth Orbit (LEO) satellite networks. However, the high mobility of satellites and the rapidly time-varying channel conditions pose significant challenges to traditional resource management schemes. Conventional optimization met...
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Multi-agent deep reinforcement learning (MADRL) offers a promising solution for routing in low Earth orbit (LEO) satellite networks. However, large inter-satellite propagation delays lead to severe state information lag in agent interactions, giving rise to decision biases and degraded routing timeliness. To this end,...
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A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system pote...
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