An Improved 3D Indoor Positioning Study with Ray Tracing Modeling for 6G Systems

Wang, Youqing; Zhao, Kun; Zheng, Zhengqi

doi:10.1007/s11036-023-02127-5

Cited by 2 publications

(3 citation statements)

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“…Thus, the complexity of the adaptive DCS-SOMP is O(LN t q c q it ), where q it is the number of iterations. The main mathematical operations are due to (7) and (8). We determine the total number of mathematical operations, q op , as follows:…”

Section: Resultsmentioning

confidence: 99%

“…First, the estimation of localization parameters extracted from the received signal. Second, using the acquired parameters from the first step, a localization method is employed to determine the position of the mobile station (MS) [5,8,9]. Regarding the first step, we highlight several aspects of the literature.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to the use of a linear array of antennas, the proposed approach can only determine the azimuth of AoA and AoD and does not enable ToA estimation. In [8], the authors conduct indoor localization using channel modeling and ray tracing. They use two Uniform Linear Arrays (ULA), one perpendicular to the other, to extract the elevation angle.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks

da Conceição,

Rocha

2023

Sensors

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In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing—Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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