Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach

Choi, Hongrok; Pack, Sangheon

doi:10.3390/s22186853

Cited by 5 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to United Nations Office for Disaster Risk Reduction (UNDRR) and Ref. [30], until 2021, there has been a loss of approximately 280 billion USD in infra-structure and resources with 103.5 million people affected globally. This is a severe situation and needs to be dealt with before it is too late.…”

Section: Discussionmentioning

confidence: 99%

A Study of Autonomous Small Satellite Constellations for Disaster Management and Deep Space Strategy

Rashed

Bang

2022

Remote Sensing

View full text Add to dashboard Cite

The complex and dynamic space environment is both exciting and challenging in this NewSpace era. In particular, Low-Earth Orbits are being realigned and reinvented for various purposes using suitable technological advancement. This paper is focused on the major parameters that can be analyzed to attain orbit control and autonomy of small-satellite constellations for real-time applications. By applying industry experience to graduate research, this work addresses the related concerns in a realistic manner. Currently, global small-satellite constellation solutions are too expensive and inaccessible for many nations to help in their data reception requirements. This issue was addressed, and some of the main aspects relating to low-cost and high-benefit technical synthesis, in addition to utilization for deep-space missions, were also discussed in detail. In conclusion, this paper demonstrated a strategic approach to optimize the coverage and performance, and reduce the cost of small-satellite constellations, compared with present day constellations, allowing the data to be relayed faster and with precision. This will benefit the industry in the development of low-cost constellations, and effectively assist in disaster management and deep-space communication relays. Autonomous orbit selection and navigation can be established for better path alignment for satellites to efficiently propagate and deliver the required data.

show abstract

Section: Discussionmentioning

confidence: 99%

A Study of Autonomous Small Satellite Constellations for Disaster Management and Deep Space Strategy

Rashed

Bang

2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…DRL, as a powerful artificial intelligence technique, offers the core advantage of autonomous learning and optimization in complex and uncertain environments. It achieves this by interacting with the environment, accumulating experiences, and progressively improving decision quality [27][28][29][30].…”

Section: Overviewmentioning

confidence: 99%

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning

Wei,

Guo,

Yang

2023

Sensors

View full text Add to dashboard Cite

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users’ services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users’ services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average.

show abstract

“…Recently, graph-based deep learning optimization, which can guarantee high performance with low complexity, has received great attention and has been applied in communication networks [ 22 ]. Deep learning methods can capture the spatial information hidden in the network topology and work well for high dynamic scenes, thus providing new ideas for satellite traffic prediction [ 23 ] and resource allocation [ 24 , 25 , 26 ]. For example, Ref.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Multi-Dimensional Resource Allocation Mechanism for Beam-Hopping in LEO Satellite Network

Guo

Han

Gong

et al. 2022

Sensors

View full text Add to dashboard Cite

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

show abstract

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach

Cited by 5 publications

References 36 publications

A Study of Autonomous Small Satellite Constellations for Disaster Management and Deep Space Strategy

A Study of Autonomous Small Satellite Constellations for Disaster Management and Deep Space Strategy

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning

An Efficient Multi-Dimensional Resource Allocation Mechanism for Beam-Hopping in LEO Satellite Network

Contact Info

Product

Resources

About