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Updated: Feb 14, 2026

Four-Dimensional Printing of Stimuli-Responsive Hydrogel-Based Soft Robots
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基于强化学习的云意识的HAPS轨迹优化在软切换混合FSO/RF合作传输系统中

Beibei Cui1,2, Shanyong Cai1,2, Liqian Wang1,2

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
概括

本研究引入了一种使用无级代码 (RCs) 和深度强化学习 (DRL) 进行混合自由空间光学/无线电频率 (FSO/RF) 通信的新软切换方法. RC-PPO方法提高了高海拔平台站 (HAPS) 的网络吞吐量和轨迹平滑性.

关键词:
深度强化学习 (DRL) 是一种深度强化学习.混合式 FSO/RF FSO/RF 接线系统邻近政策优化 (PPO)轨道优化轨道优化

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