多径环境下用于挤压天线系统的上行链路定位
Uplink Positioning for PASS in Multipath Environments
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中文总结 AI 辅助
研究多径环境下PASS的上行链路定位，提出基于矩阵铅笔和秩-1的测距算法及两阶段加权非线性最小二乘定位算法，通过理论分析和数值结果表明算法性能，如MP算法精度高、秩-1算法复杂度低等。
中文摘要 AI 辅助
挤压天线系统（PASS）通过在用户附近激活或放置挤压天线（PA）来增强无线传播，因此准确的上行链路定位对于高效通信至关重要。本文为多径环境下的PASS建立了一个上行链路多载波定位框架。提出了基于矩阵铅笔（MP）和低复杂度秩-1测距算法来估计PA与用户之间的距离。对于基于MP的测距算法，通过利用汉克尔矩阵的平移不变性将视距（LoS）分量与非视距分量分离，从而实现准确的距离估计。对于秩-1测距算法，通过截断奇异值分解直接分离出主导的LoS延迟，从而避免矩阵求逆。随后，设计了一种两阶段加权非线性最小二乘（WNLS）定位算法来估计用户的三维位置。为了深入了解，对所提出的测距和定位算法进行了全面的理论性能分析。推导了闭式测距方差和位置误差界（PEB）以揭示误差传播机制。数值结果表明：i）基于MP的算法比基于秩-1的算法具有更高的精度和鲁棒性，而基于秩-1的算法具有较低的计算复杂度。ii）基于MP的算法的定位误差与推导的PEB遵循相同的趋势，而秩-1算法由于多径偏差表现出误差下限。iii）MP算法的定位精度随着子载波数量的增加而提高。
英文摘要
Pinching-antenna systems (PASS) enhance wireless propagation by activating or placing pinching antennas (PAs) near users. Therefore, accurate uplink positioning is essential for efficient communication. In this paper, an uplink multi-carrier positioning framework is established for PASS in multipath environments. Matrix pencil (MP)-based and low-complexity Rank-1 ranging algorithms are proposed to estimate the distances between the PAs and the user. For the MP-based ranging algorithm, the line-of-sight (LoS) component is separated from non-line-of-sight components by exploiting the shift-invariance property of the Hankel matrix, thereby enabling accurate distance estimation. For the Rank-1 ranging algorithm, the dominant LoS delay is directly isolated through truncated singular value decomposition, thereby avoiding matrix inversions. Subsequently, a two-stage weighted nonlinear least-squares (WNLS) positioning algorithm is designed to estimate the three-dimensional user position. To gain further insights, a comprehensive theoretical performance analysis of the proposed ranging and positioning algorithms is conducted. The closed-form ranging variances and position error bound (PEB) are derived to reveal the error propagation mechanism. Numerical results demonstrate that: i) The MP-based algorithm achieves higher accuracy and robustness than the Rank-1-based algorithm, while the Rank-1-based algorithm has lower computational complexity. ii) The positioning error of the MP-based algorithm follows the same trend as the derived PEB, whereas the Rank-1 algorithm exhibits an error floor due to multipath bias. iii) The positioning accuracy of the MP algorithm improves as the number of subcarriers increases.
