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可重构智能表面辅助的设备到设备（D2D）系统的定位性能分析与算法设计

Localization Performance Analysis and Algorithm Design of Reconfigurable Intelligent Surface-Assisted D2D Systems.

作者信息

Wang Mengke, Lv Tiejun, Huang Pingmu, Lin Zhipeng

机构信息

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

出版信息

Sensors (Basel). 2024 Jun 6;24(11):3694. doi: 10.3390/s24113694.

DOI:10.3390/s24113694
PMID:38894484
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11175182/
Abstract

The research on high-precision and all-scenario localization using the millimeter-wave (mmWave) band is of great urgency. Due to the characteristics of mmWave, blockages make the localization task more complex. This paper proposes a cooperative localization system among user equipment (UEs) assisted by reconfigurable intelligent surfaces (RISs), which considers device-to-device (D2D) communication. RISs are used as anchor points, and position estimation is achieved through signal exchanges between UEs. Firstly, we establish a localization model based on this system and derive the UEs' positioning error bound (PEB) as a performance metric. Then, a UE-RIS joint beamforming design is proposed to optimize channel state information (CSI) with the objective of achieving the minimum PEB. Finally, simulation analysis demonstrates the advantages of the proposed scheme over RIS-assisted base station positioning, achieving centimeter-level accuracy with a 10 dBm lower transmission power.

摘要

利用毫米波（mmWave）频段进行高精度全场景定位的研究迫在眉睫。由于毫米波的特性，障碍物使得定位任务更加复杂。本文提出了一种由可重构智能表面（RIS）辅助的用户设备（UE）之间的协作定位系统，该系统考虑了设备到设备（D2D）通信。RIS被用作锚点，通过UE之间的信号交换来实现位置估计。首先，我们基于该系统建立了一个定位模型，并推导了UE的定位误差界（PEB）作为性能指标。然后，提出了一种UE-RIS联合波束成形设计，以优化信道状态信息（CSI），目标是实现最小的PEB。最后，仿真分析证明了所提方案相对于RIS辅助基站定位的优势，在传输功率低10 dBm的情况下实现了厘米级精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/13451aeb9a21/sensors-24-03694-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/b0525503acf2/sensors-24-03694-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/ae1ba067d964/sensors-24-03694-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/d436e73647b8/sensors-24-03694-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/2e50bc65dc43/sensors-24-03694-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/c0df31a183d5/sensors-24-03694-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/6b9ea5f7ba7c/sensors-24-03694-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/0935f56e0125/sensors-24-03694-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/13451aeb9a21/sensors-24-03694-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/b0525503acf2/sensors-24-03694-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/ae1ba067d964/sensors-24-03694-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/d436e73647b8/sensors-24-03694-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/2e50bc65dc43/sensors-24-03694-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/c0df31a183d5/sensors-24-03694-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/6b9ea5f7ba7c/sensors-24-03694-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/0935f56e0125/sensors-24-03694-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6699/11175182/13451aeb9a21/sensors-24-03694-g008.jpg

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