论文标题
学习优化：在MB-HTS网络中平衡两个冲突指标
Learning to Optimize: Balancing Two Conflict Metrics in MB-HTS Networks
论文作者
论文摘要
对于多光束高吞吐量（MB-HTS）地球静止（GEO）卫星网络，当用户的需求无法完全满足时，会出现拥塞。本文通过制定和解决拥塞控制下的功率分配策略来招募用户来提高系统性能。制定了新的多目标优化，以平衡总和数据吞吐量和满意的用户集。之后，我们提出了两种不同的解决方案，这些解决方案有效地解决了多目标最大化问题：基于模型的解决方案利用加权总和方法来增强需求满意的用户的数量，而监督的学习解决方案通过将优化结构继承为连续映射，从而提供了低计算的复杂性设计。仿真结果证明了我们的解决方案是否有效地应对拥塞，并且比以前的其他工作要优于数据吞吐量需求。
For multi-beam high throughput (MB-HTS) geostationary (GEO) satellite networks, the congestion appears when user's demands cannot be fully satisfied. This paper boosts the system performance by formulating and solving the power allocation strategies under the congestion control to admit users. A new multi-objective optimization is formulated to balance the sum data throughput and the satisfied user set. After that, we come up with two different solutions, which efficiently tackle the multi-objective maximization problem: The model-based solution utilizes the weighted sum method to enhance the number of demand-satisfied users, whilst the supervised learning solution offers a low-computational complexity design by inheriting optimization structures as continuous mappings. Simulation results verify that our solutions effectively copes with the congestion and outperforms the data throughput demand than the other previous works.
