离网稀疏贝叶斯式学习用于在NLoS下在RIS辅助的MIMO-OFDM中进行通道估计和定位.
1Bor Vocational School, Nigde Ömer Halisdemir University, Nigde 51700, Turkey.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了可重新配置智能表面 (RIS) 6G无线系统的混合框架,通过稀疏的贝叶斯学习来增强通道估计和移动站本地化,以在非视线条件下提高准确性.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 电磁学 电磁学 电磁学 电磁学
背景情况:
- 可重新配置的智能表面 (RIS) 对6G无线系统至关重要,但它们的被动性和非视线 (NLoS) 挑战使上链通道估计和定位变得复杂.
- 密集的城市环境加剧了这些挑战,原因是信号阻塞和多路径传播.
研究的目的:
- 为可重配置智能表面 (RIS) 辅助的6G多输入多输出 (MIMO) 直角频率分割复杂化 (OFDM) 系统开发一个强大的混合通道参数估计框架.
- 为应对NLoS城市环境中通道估计和移动站 (MS) 定位的挑战.
主要方法:
- 使用双散角结构建模RIS-MS通道.
- 实现一个混合框架,将同时正交对应追求 (SOMP) 结合起来,用于粗支持估计和基于变量贝叶斯预期最大化 (VBEM) 的离网稀疏贝叶斯学习 (OG-SBL) 进行改进.
- 使用到达角 (AoA) - 离开角 (AoD) 匹配算法和基于NLoS多路径几何学的3D定位程序.
主要成果:
- 拟议的框架实现了高角度分辨率和精确的定位精度, 97%的结果在0.01米以内.
- 在40dB的信号噪声比率 (SNR) 中显示低频道估计误差为0.0046%.
- 在城市环境中有效处理NLoS条件.
结论:
- 混合框架在RIS辅助的6G MIMO-OFDM系统中显著提高了通道估计和本地化性能.
- 基于VBEM的OG-SBL精细化和AoA-AoD匹配对准确的参数估计和3D定位有效.
- 这种方法为在具有挑战性的NLoS场景中可靠的无线通信提供了有希望的解决方案.
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