优化STAR-RIS的深度学习,以提高6G无线网络的数据速率和能源效率
Amal Megahed1, Ahmed M Abd El-Haleem2, Mahmoud M Elmesalawy2
1Department of Electronics and Communications Engineering, Faculty of Engineering, Helwan University, Cairo, 11792, Egypt. Amal.Megahed2426@h-eng.helwan.edu.eg.
Scientific reports
|July 20, 2025
概括
同时传输和反射的可重新配置智能表面 (STAR-RISs) 为6G网络提供全空间覆盖. 研究表明,几乎被动的STAR-RIS在高干扰下可以超过活跃系统,突出显示了能源效率的好处.
科学领域:
- 无线通信无线通信
- 智能表面是一种智能表面.
- 6G 网络 6G 网络
背景情况:
- 传统的可重新配置的智能表面 (RIS) 仅限于半个空间的覆盖范围.
- 同时发射和反射RIS (STAR-RIS) 能够覆盖整个空间,为信号传播控制提供新的自由度.
- STAR-RIS技术是未来6G无线网络的一个关键推动因素.
研究的目的:
- 评估STAR-RIS在6G无线网络中的性能.
- 将近被动的STAR-RIS (NP-STAR) 和活跃的STAR (ASTAR) 与近被动的RIS (NP-RIS) 和活跃的RIS (ARIS) 基准进行比较.
- 通过使用基于深度学习 (DL) 的优化来评估可实现的数据速率和光谱能效 (SEE).
主要方法:
- 为了分析系统性能,进行了广泛的模拟.
- 基于深度学习 (DL) 的优化被用于绩效评估.
- 在各种配置中评估了性能指标,包括可实现的数据速率和光谱能效 (SEE).
主要成果:
- 在高干扰环境中,NP-STAR配置通过减轻干扰放大而表现出比ASTAR更好的性能.
- 积极的RIS实现与近乎被动的相比,能耗更高,导致光谱能效 (SEE) 降低.
- 深度学习方法有效地接近精灵辅助的性能界限,具有足够的训练数据,特别是在复杂的道场景中.
结论:
- 在6G无线网络中,STAR-RIS技术具有巨大的潜力,可提供全空间覆盖.
- 管理基站发射功率对于干扰控制至关重要,活跃的STAR-RIS在最佳通道条件中表现出色.
- 解决现实世界性能,能源光谱效率权衡和DL优化的研究差距对于实际的STAR-RIS部署至关重要.
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