Attention-Based Deep Reinforcement Learning for Edge User Allocation

Chang, J. Morris; Wang, Jian; Li, Bing; Zhao, Yuqi; Li, Duantengchuan

doi:10.1109/tnsm.2023.3292272

Cited by 3 publications

(1 citation statement)

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“…In this scenario, akin to the edge user allocation (EUA) problem mentioned in [39], we can model the SSA problem as an MDP as shown in Figure 4. This includes the satellite network state S, satellite service allocation actions A, and the effectiveness of service allocation R. Therefore, the length of the Markov chain equals the number of users requesting services in each timeslot.…”

Section: Problem Statementmentioning

confidence: 99%

A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks

Zhao,

Zhou,

Chen

et al. 2024

Aerospace

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Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users. In LEO constellations, a significant deployment of computationally capable satellites is orchestrated to offer enhanced computational resources. Challenges arise in the optimal allocation of terminal services to the most suitable satellite due to overlapping coverage among neighboring satellites, compounded by constraints on satellite energy and computational resources. The satellite service allocation (SSA) problem is recognized as NP-hard, yet assessing allocation methods through results allows for the application of deep reinforcement learning (DRL) to obtain improved solutions, partially addressing the SSA challenge. In this paper, we introduce a satellite computing capability model to quantify satellite computational resources. A DRL model is proposed to address service demands, computational resources, and resolve service allocation conflicts, strategically placing each service on appropriate servers. Through simulation experiments, numerical results demonstrate the superiority of our proposed method over baseline approaches in service allocation and satellite resource utilization, showcasing advancements in this field.

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Section: Problem Statementmentioning

confidence: 99%