数据辅助的最大概率关节角度和延迟估计器在正交频率分割多路单输入多路输出通道上基于新灰狼优化嵌入重要性采样.
Maha Abdelkhalek1, Souheib Ben Amor1,2, Sofiène Affes1
1The Wireless Lab, EMT Centre, Institut National de la Recherche Scientifique (INRS), Montreal, QC H5A 1K6, Canada.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一个新的数据辅助联合角度和延迟 (JADE) 估计器,GWOEIS,改进了使用重要性采样 (IS) 的灰狼优化 (GWO),以提高无线通道估计的准确性和速度.
科学领域:
- 信号处理 信号处理
- 无线通信无线通信
- 优化算法 优化算法
背景情况:
- 准确估计到达角度 (AoA) 和时间延迟 (TD) 对无线通信系统至关重要.
- 传统的方法,如灰狼优化 (GWO),由于随机初始化和缓慢的融合,可能是低效的.
- 具有单输入多输出通道 (SIMO) 的直角频率分割复合 (OFDM) 系统在多路径环境中面临着挑战.
研究的目的:
- 提出一个新的数据辅助 (DA) 联合角度和延迟 (JADE) 最大概率 (ML) 估计器.
- 通过整合重要性采样 (IS) 概念来增强灰狼优化 (GWO) 算法,创建GWOEIS方法.
- 为了提高OFDM-SIMO通道的估计准确性,分辨率能力和融合速度.
主要方法:
- 通过将重要性采样 (IS) 嵌入到灰狼优化 (GWO) 算法中,开发了GWOEIS.
- 修改并动态更新GWO收因子,使用来自IS的累积分布函数 (CDF).
- 利用简化的重要性函数进行可靠的初始估计,提高了搜索效率.
主要成果:
- 与传统的GWO相比,GWOEIS展示了全球最佳性和优越的分辨率能力.
- 拟议的方法通过提供可靠的初步估计,实现更快的趋同.
- 模拟证实了精度和速度的显著改进,GWOEIS甚至在信号噪声比 (SNR) 较低的情况下也达到克拉梅尔-拉奥下界 (CRLB).
结论:
- 对于JADE估计,GWOEIS比传统的GWO提供了显著的改进.
- 集成IS显著提高了GWO算法的效率和性能.
- 新的估计器在具有挑战性的无线通道条件下提供了近乎最佳的性能.
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