Task Scheduling Under a Novel Framework for Data Relay Satellite Network via Deep Reinforcement Learning

Li, Jiaxing; Wu, Guohua; Liao, Tianjun; Fan, Mingfeng; Mao, Xiao; Pedrycz, Witold

doi:10.1109/tvt.2022.3233358

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…Resource scheduling is a crucial aspect of satellite operation control, which is crucial for improving efficiency and ensuring quality of service. Scholars have solved the problem of multi-satellite resource scheduling using varied approaches such as optimization modeling, exact algorithms, heuristic algorithms, meta-heuristic algorithms [28,29], and reinforcement learning algorithms [30][31][32][33].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

Li,

Guo,

Meng

et al. 2023

Electronics

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Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users’ demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users’ demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance.

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Section: Literature Reviewmentioning

confidence: 99%

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

Li,

Guo,

Meng

et al. 2023

Electronics

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“…For the last few decades, to improve the performance of wireless communication between a source node (SN) and destination node (DN), cooperative communications that employ a relay node (RN) have been extensively studied [1]- [28]. The applications of RN have recently extended from the traditional terrestrial nodes to the non-terrestrial nodes, such as the unmanned aerial vehicles [29] and satellites [30]. Various types of relaying protocols and relays exist.…”

Section: Introductionmentioning

confidence: 99%

SNR Threshold-Based Relay Association and Random Phase Rotation for Cooperative Communication

Joung

Park

et al. 2023

IEEE Access

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In this study, a cooperative communication system that employs multiple decode-and-forward relay nodes (RNs), in which the associated/active RNs perform phase rotation of the regenerated signals before retransmitting them to a destination node (DN), is examined. In the first phase, i.e., communication from a source node to RNs, a received signal-to-noise ratio (SNR) threshold-based RN association method is proposed. The optimal SNR thresholds are designed to maximize the bit-error-rate (BER) performance at the DN under various communication environments, such as modulation types and channel code rates. Furthermore, the number of phase rotations (PRs) in a frame is examined. Intensive numerical results show that more PRs in a frame provide better BER performance at the DN, irrespective of the communication environments. This study provides a valuable guideline for designing practical cooperative networks with multiple decode-and-forward RNs with PRs.INDEX TERMS Cooperative communications, decode-and-forward relays, phase rotation, frame structure design.

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A Real-Time Scheduling Method for Emergency Tasks in Satellite-Ground System

Wu,

Yu,

Chen

et al. 2024

Proceedings of Ninth International Congress on Information and Communication Technology

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Task Scheduling Under a Novel Framework for Data Relay Satellite Network via Deep Reinforcement Learning

Cited by 10 publications

References 29 publications

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

SNR Threshold-Based Relay Association and Random Phase Rotation for Cooperative Communication

A Real-Time Scheduling Method for Emergency Tasks in Satellite-Ground System

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