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Volodymyr Vizitiv

Publications and source records attributed to Volodymyr Vizitiv.

3 recordsLinked to original sources

Empirical Analysis of Near-Field Beam Shaping for Blockage Events

Millimeter-wave (mmWave) to sub-terahertz (sub-THz) links can realize high data rates, yet are highly vulnerable to dynamic blockages due to high directivity, limited multipath, and dependence on line-of-sight (LoS) propagation. While structured and self-healing beams are promising solutions, it remains unclear how beam types perform under dynamic blockage. In this paper, we present an empirical analysis of structured near-field beams using two transmission policies. At one extreme, we study beam resilience without obstacle adaptation. At the other extreme, we empirically optimize beam parameters to study the limits of multiple beam types under idealized adaptation. We employ numerical analysis based on the angular spectrum method (ASM) and experimental validation using a sub-THz time-domain spectroscopy (TDS) platform to characterize beam performance over complete blockage events. For the scenarios considered, despite lacking self-healing, focused beams provide the strongest resilience when beam parameters are not adapted during a blockage event (i.e., when all beams are optimized only a priori). In contrast, when optimally adapted to obstacle position, curved beams provide the best performance. Lastly, although Bessel beams benefit from self-healing, this property alone can be insufficient to outperform optimized curved and focused beams. These findings provide guidance for blockage-aware beam selection and adaptation protocols.

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6D Rigid Body Localization and Velocity Estimation via Gaussian Belief Propagation

We propose a novel message-passing solution to the sixth-dimensional (6D) moving rigid body localization (RBL) problem, in which the three-dimensional (3D) translation vector and rotation angles, as well as their corresponding translational and angular velocities, are all estimated by only utilizing the relative range and Doppler measurements between the "anchor" sensors located at an 3D (rigid body) observer and the "target" sensors of another rigid body. The proposed method is based on a bilinear Gaussian belief propagation (GaBP) framework, employed to estimate the absolute sensor positions and velocities using a range- and Doppler-based received signal model, which is then utilized in the reconstruction of the RBL transformation model, linearized under a small-angle approximation. The method further incorporates a second bivariate GaBP designed to directly estimate the 3D rotation angles and translation vectors, including an interference cancellation (IC) refinement stage to improve the angle estimation performance, followed by the estimation of the angular and the translational velocities. The effectiveness of the proposed method is verified via simulations, which confirms its improved performance compared to equivalent state-of-the-art (SotA) techniques.

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Belief Propagation-based Rotation and Translation Estimation for Rigid Body Localization

We propose a novel solution to the rigid body localization (RBL) problem, in which the three-dimensional (3D) rotation and translation is estimated by only utilizing the range measurements between the wireless sensors on the rigid body and the anchor sensors. The proposed framework first constructs a linear Gaussian belief propagation (GaBP) algorithm to estimate the absolute sensor positions utilizing the range-based received signal model, which is used for the reconstruction of the RBL transformation model, linearized with a small-angle approximation. In light of the reformulated system, a second bivariate GaBP is designed to directly estimate the 3D rotation angles and translation distances, with an interference cancellation (IC) refinement to improve the angle estimation performance. The effectiveness of the proposed method is verified via numerical simulations, highlighting the superior performance of the proposed method against the state-of-the-art (SotA) techniques for the position, rotation, and translation estimation performance.

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