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arXiv 2401.06334eess.SP
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基于全息MIMO的统一近场与远场定位

Unified Near-field and Far-field Localization with Holographic MIMO

Mengyuan Cao, Haobo Zhang, Boya Di, Hongliang Zhang

AI总结:

针对RIS定位中近场与远场假设不统一导致的复杂度或精度问题,本文提出基于网格划分的统一近场与远场定位方法,并设计位置估计与RIS相移优化算法,仿真验证了其有效性。

摘要:

利用全息多输入多输出表面(如可重构智能表面(RIS))进行定位,因其在非视距条件下能够准确确定用户位置而受到越来越多的关注。然而,现有的基于RIS的定位方法假设用户仅位于近场(NF)或远场(FF)区域,当这些方法应用于整个区域时,分别会导致高复杂度或低定位精度。本文针对基于RIS的定位系统提出了一种统一的近场与远场定位方法,以克服上述问题。具体而言,近场和远场区域都被划分为网格。RIS将来自用户的信号反射到基站(BS),然后基站利用接收到的信号确定用户所在的网格。与现有的仅近场或仅远场方案相比,位置估计方法和RIS相移优化算法的设计更具挑战性,因为它们基于混合的近场和远场模型。为应对这些挑战,我们针对位置估计和RIS相移分别建立了优化问题,并设计了两种算法来有效求解所建立的问题。仿真结果验证了所提方法的有效性。

英文摘要:

Localization which uses holographic multiple input multiple output surface such as reconfigurable intelligent surface (RIS) has gained increasing attention due to its ability to accurately localize users in non-line-of-sight conditions. However, existing RIS-enabled localization methods assume the users at either the near-field (NF) or the far-field (FF) region, which results in high complexity or low localization accuracy, respectively, when they are applied in the whole area. In this paper, a unified NF and FF localization method is proposed for the RIS-enabled localization system to overcome the above issue. Specifically, the NF and FF regions are both divided into grids. The RIS reflects the signals from the user to the base station~(BS), and then the BS uses the received signals to determine the grid where the user is located. Compared with existing NF- or FF-only schemes, the design of the location estimation method and the RIS phase shift optimization algorithm is more challenging because they are based on a hybrid NF and FF model. To tackle these challenges, we formulate the optimization problems for location estimation and RIS phase shifts, and design two algorithms to effectively solve the formulated problems, respectively. The effectiveness of the proposed method is verified through simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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