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基于决策性能评估的多波束低地球轨道卫星网络资源分配算法

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为了解决多波束低地球轨道(LEO)卫星波束间同频干扰、频谱短缺、业务量分布不均等问题,针对单一决策网络缺乏自我修正能力、容易陷入局部最优解、无法充分考虑长期影响等弊端,提出了一种基于决策性能评估的资源分配算法.该算法引入不同用户的业务满足指数来衡量系统的公平性,在考虑公平性的前提下优化系统的吞吐量性能,并将该优化问题建模为多目标优化问题.将具有时间相关性的连续资源分配过程建模为马尔可夫过程,提出基于决策性能评估的网络资源分配算法来解决该问题.所提算法可以根据评估网络的评估结果调整决策网络参数,从而优化资源分配方案,同时更新评估网络自身参数.通过迭代优化的方式,实现决策网络的准确预测.仿真结果表明,所提算法在吞吐量性能和公平性方面优于传统资源分配算法.
Resource allocation algorithm for multi-beam LEO satellite based on decision performance evaluation
To address challenges such as co-frequency interference,spectrum scarcity,and uneven traffic distribution in multi-beam LEO satellites,a resource allocation algorithm based on decision performance evaluation was proposed.The system fairness was measured by a user satisfaction index and the system throughput was optimized while considering fairness.The optimization problem was modeled as a multi-objective optimization.The continuous resource allocation process with temporal correlation was modeled as a Markov decision process,and a decision-evaluation dual-network al-gorithm was proposed to solve it.The decision network parameters were adjusted based on evaluation network results to optimize resource allocation and update the evaluation network parameters.Through iterative optimization,the decision network achieved accurate predictions.Simulation results show that the proposed algorithm outperforms traditional re-source allocation algorithms in terms of throughput and fairness.

multi-beam satellitedeep reinforcement learningmulti-objective optimizationresource management

王朝炜、庞明亮、王粟、赵玲莉、高飞飞、崔高峰、王卫东

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北京邮电大学电子工程学院,北京 100875

中国移动通信集团有限公司,北京 100032

清华大学自动化系,北京 100084

多波束卫星 深度强化学习 多目标优化 资源管理

重庆市自然科学基金资助项目北京邮电大学博士生创新基金资助项目

CSTB2023NSCQ-LZX0118CX2023139

2024

通信学报
中国通信学会

通信学报

CSTPCD北大核心
影响因子:1.265
ISSN:1000-436X
年,卷(期):2024.45(7)
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