Analysis on the Neural Network-aided Satellite Resource Allocation Schemes

Gyuseong Jo, Satya Chan, S. K. Shin, D. Oh
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引用次数: 1

Abstract

Satellite systems can efficiently utilize expensive and limited bandwidth and power resources, by reusing frequency bands over multibeams with provision of optimum resource allocation. This paper provides comparative analysis on the resource allocation schemes for frequency reusing multibeam satellite systems under interference-limited condition. After reviewing recent works on machine learning-aided schemes, we propose a new idea to enhance the performance. The performance estimation results investigated in this paper reveal that the proposed scheme can enhance the performance compared to the existing method.
神经网络辅助卫星资源分配方案分析
卫星系统通过在多波束上重复使用频带并提供最佳的资源分配,可以有效地利用昂贵而有限的带宽和功率资源。本文对干扰限制条件下频率复用多波束卫星系统的资源分配方案进行了比较分析。在回顾了最近关于机器学习辅助方案的工作之后,我们提出了一个新的想法来提高性能。本文的性能估计结果表明,与现有方法相比,该方案可以提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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