Computer Vision Enabled Sub-THz Radio Channel Characterization of Dynamic Objects
Regmi, Ankit; Susarla, Praneeth; Zhang, Peize; Tervo, Nuutti; Lopez, Miguel Bordallo; Silven, Olli; Kyösti, Pekka; Leinonen, Marko E.; Pärssinen, Aarno (2024-04-26)
IEEE
26.04.2024
A. Regmi et al., "Computer Vision Enabled Sub-THz Radio Channel Characterization of Dynamic Objects," 2024 18th European Conference on Antennas and Propagation (EuCAP), Glasgow, United Kingdom, 2024, pp. 1-5, doi: 10.23919/EuCAP60739.2024.10501593
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405314122
https://urn.fi/URN:NBN:fi:oulu-202405314122
Tiivistelmä
Abstract
To enable a wide range of joint communications and sensing applications, future channel models must support dynamic variations of the propagation environment. Moreover, when discovering sensing applications, it is essential to connect physical objects and actions to the radio channel characteristics. In this paper, we perform computer vision (CV) aided automatic mapping and showcase that channel measurements can be linked to the dynamic actions and objects in the environment, thus supporting sensing applications. The proposed method employs a vector network analyzer-based channel measurement system operating at 300 GHz and a camera to enable CV for tracking the object in the radio link. Using human hand as the blockage object, the CV-extracted coordinates and the measured channel responses are combined to generate radio channel footprints for different hand movements.
To enable a wide range of joint communications and sensing applications, future channel models must support dynamic variations of the propagation environment. Moreover, when discovering sensing applications, it is essential to connect physical objects and actions to the radio channel characteristics. In this paper, we perform computer vision (CV) aided automatic mapping and showcase that channel measurements can be linked to the dynamic actions and objects in the environment, thus supporting sensing applications. The proposed method employs a vector network analyzer-based channel measurement system operating at 300 GHz and a camera to enable CV for tracking the object in the radio link. Using human hand as the blockage object, the CV-extracted coordinates and the measured channel responses are combined to generate radio channel footprints for different hand movements.
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