Analysis of the Spatial Smoothing Influence on the Perfomance of 2D-MUSIC for Ultra-Dense Networks

Grishin, Ilya; Fokin, Grigoriy; Sevidov, Vladimir; Okuneva, Darina

doi:10.1109/weconf57201.2023.10148006

Cited by 2 publications

(2 citation statements)

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“…Additionally, the DCS-SOMP method provides only a coarse parameter estimate, demanding further fine-tuning using the SAGE method. In [11], the authors propose a 2D-AoA and 2D-AoD estimation using the MUSIC algorithm. The method utilizes a channel model with a rectangular Uniform Rectangular Array (URA).…”

Section: Related Workmentioning

confidence: 99%

“…However, their proposal does not estimate ToA and only allows coarse angle estimation. Similarly to [5], the proposal in [11] requires fine-tuning techniques to provide accurate parameter estimations. In [12], the authors propose a technique named spatial spectrum fusion estimation and localization (SSFEAL) for performing 2D-AoA estimation using UCA in a MIMO mmWave channel.…”

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: Related Workmentioning

confidence: 99%