Sequential dynamic resource allocation in multi-beam satellite systems: A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites (MBS) is an efficient Dynamic Resource Allocation (DRA) strategy. This paper presents a learning-based Hybrid-Act...

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Bibliographic Details
Published in:Chinese journal of aeronautics 2023-06, Vol.36 (6), p.288-301
Main Authors: HUANG, Yixin, WU, Shufan, ZENG, Zhankui, KANG, Zeyu, MU, Zhongcheng, HUANG, Hai
Format: Article
Language:English
Subjects:
Beam hopping
Deep reinforcement learning
Dynamic resource allocation
Mixed-integer programming
Multi-beam satellite systems
Multi-objective optimization
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Summary:Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites (MBS) is an efficient Dynamic Resource Allocation (DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network (HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space, HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly. To pursue multiple long-term QoS requirements, HADQN adopts a multi-objective optimization method to decrease system transmission delay, loss ratio of data packets and power consumption load simultaneously. Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.
ISSN:1000-9361
DOI:10.1016/j.cja.2022.12.012