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一种结构化的稀疏贝叶斯道估计方法,用于正交时频空间调制.

Mi Zhang1, Xiaochen Xia1, Kui Xu1

  • 1School of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

坐标时频空间 (OTFS) 调制辅助集成传感和通信 (ISAC). 本研究引入了贝叶斯式学习方法,用于OTFS-ISAC系统中准确的通道估计,在低SNR条件下提高性能.

关键词:
频道估计 频道估计集成传感和通信.直角时间频率空间调制.结构化稀疏贝叶斯式学习

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科学领域:

  • 无线通信无线通信是无线的.
  • 信号处理 信号处理
  • 综合传感和通信 (ISAC) 系统

背景情况:

  • 坐标时频空间 (OTFS) 调制适用于高流动性场景和集成传感和通信 (ISAC).
  • 准确的通道采集对于OTFS-ISAC系统至关重要,但由于分数多普勒位移而具有挑战性.
  • 现有的方法在基于OTFS的ISAC中难以高效地进行通道估计.

研究的目的:

  • 为OTFS-ISAC系统开发一个准确和高效的通道估计方法.
  • 为了应对OTFS信号中分数多普勒频率转移所带来的挑战.
  • 提高ISAC系统的性能,特别是在低信号噪声比 (SNR) 的环境中.

主要方法:

  • 对OTFS信号的延迟多普勒 (DD) 域中频道稀疏结构的导出.
  • 关于用于道估计的结构化贝叶斯学习方法的建议.
  • 开发一个连续的最大化-最小化 (SMM) 算法用于后方通道估计计算.

主要成果:

  • 提出的结构化贝叶斯式学习方法准确地估计了延迟-多普勒通道.
  • 该SMM算法有效计算后方通道估计.
  • 新方法显著优于现有方案,特别是在较低的SNR.

结论:

  • 建议的结构化贝叶斯学习方法在OTFS-ISAC系统中对道估计是有效的.
  • 该方法与参考方案相比,表现优越,特别是在具有挑战性的低SNR条件下.
  • 这项工作有助于推进使用OTFS调制的可靠和高效的ISAC系统.