学习优化卫星灵活有效载荷

M. Vázquez, P. Henarejos, A. Pérez-Neira
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引用次数: 0

摘要

针对具有柔性载荷的卫星系统,提出了一种优化技术。与当前卫星的单波束容量是固定的不同,即将到来的有效载荷将具有带宽和功率分配重新配置能力,允许运营商修改提供的容量。本文以通用灵活负载架构为前提,介绍了一种优化技术,该技术能够提供满足用户终端速率要求的有效带宽和功率分配。此外,我们引入了一种深度学习回归算法,能够以非常低的计算复杂性再现所提出的优化技术的映射。通过使用优化技术的输出作为基础真理,我们设计了一个与优化问题非常相似的深度神经网络,但大大减少了计算时间。数值结果显示了所提出的技术的好处,特别是,我们观察到与经典优化技术相比,使用深度学习方法的计算时间减少了两个数量级,同时保持了几乎相同的性能。
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Learning to Optimize Satellite Flexible Payloads
This paper proposes an optimization technique for satellite systems with flexible payloads. Unlike current satellites whose per-beam capacity is fixed, forthcoming payloads will have bandwidth and power allocation reconfiguration capabilities allowing the operators to modify the offered capacity. Assuming a generic flexible payload architecture, this paper introduces an op-timization technique that is able to provide an efficient bandwidth and power allocation that fulfil the user terminals rate requests. Furthermore, we introduce a deep learning regression algorithm able to reproduce the mapping of the proposed optimization technique with a very reduced computational complexity. By using the output of the optimization technique as ground truth, we design a deep neural network that behaves very similar to the optimization problem yet with a dramatically reduced computational time. Numerical results show the benefits of the proposed technique and in particular, we observe two order of magnitude computational time decrease when using the deep learning approach compared to the classical optimization technique yet preserving almost the same performance.
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