混合部署优化算法用于可重新配置的智能表面
Yifan Lin1, Xinwei Lin2, Zhiyu Han1
1The Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究介绍了一种混合算法,用于高效的可重新配置智能表面 (RIS) 部署,通过优化信号增益来改善阴影区域的无线通信质量.
科学领域:
- 无线通信是一种无线通信.
- 智能表面是一种智能表面.
- 优化算法的优化算法
背景情况:
- 可重新配置的智能表面 (RIS) 是关键的6G技术.
- 在传感器通信系统中集成RIS支持定位和传感.
- 高效的RIS部署对于减轻无线通信盲点至关重要.
研究的目的:
- 为高效的RIS部署提出混合优化算法.
- 在RIS分配中解决NP-hard组合式优化问题.
- 在阴影区域改善信号增益和通信质量.
主要方法:
- 将优化问题分解成两个阶段:贪策略和分支和边界 (BnB).
- 贪的策略将本地最佳的RIS分配给阴影区域,以确保覆盖范围的完整性.
- BnB算法优化了全球RIS部署,以最大限度地提高信号收益.
主要成果:
- 混合算法减少了大规模问题的计算复杂性.
- 贪的阶段确保了公平的覆盖.
- 与随机部署相比,基于BnB的优化在阴影区域实现了高达56.85%的平均SINR增益.
结论:
- 拟议的混合算法有效地优化了RIS部署.
- 改进的SINR增益提高了阴影区域用户的通信质量.
- 通过优化RIS部署,整体网络性能得到了显著的改善.
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