3-D Geometric-Based Indoor Stochastic Modeling Using Inverse Parabolic Scatterer Distribution

This article presents a 3-D indoor, geometric-based, stochastic channel model (GSCM) operating in the mm-wave frequency band, imitating the use of 5G technology in smartphones and wireless communication among devices (including IoT) in an indoor environment. The model incorporates a 3-D inverse parabolic distribution of the scatterers to output exact closed-form expressions of the joint and marginal probability density functions (PDFs) of the random variables: time of arrival (ToA) and angles of arrival (AoAs) at the receiver. The model is then verified through rigorous simulations in ray-tracing software with different geometric configurations of the receiver, by comparing the PDFs obtained through simulations with the theoretically calculated PDFs from the model, to prove its novelty and mathematical accuracy.

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Publication Details

Journal
IEEE Transactions on Antennas and Propagation
Published
2024-08-12
DOI
https://doi.org/10.1109/tap.2024.3438884
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.21

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article

3-D Geometric-Based Indoor Stochastic Modeling Using Inverse Parabolic Scatterer Distribution

Jayanta Mukherjee, Akshay Kaushal, Vipin W. Paradkar
1 citations
IEEE Transactions on Antennas and Propagation
Remote Sensing and LiDAR Applications
0.21
article

3-D Geometric-Based Indoor Stochastic Modeling Using Inverse Parabolic Scatterer Distribution

Jayanta Mukherjee, Akshay Kaushal, Vipin W. Paradkar
article en
1 citations

Abstract

This article presents a 3-D indoor, geometric-based, stochastic channel model (GSCM) operating in the mm-wave frequency band, imitating the use of 5G technology in smartphones and wireless communication among devices (including IoT) in an indoor environment. The model incorporates a 3-D inverse parabolic distribution of the scatterers to output exact closed-form expressions of the joint and marginal probability density functions (PDFs) of the random variables: time of arrival (ToA) and angles of arrival (AoAs) at the receiver. The model is then verified through rigorous simulations in ray-tracing software with different geometric configurations of the receiver, by comparing the PDFs obtained through simulations with the theoretically calculated PDFs from the model, to prove its novelty and mathematical accuracy.

IEEE Transactions on Antennas and PropagationVol. 72(9)
Indian Institute of Technology Bombay (IN)
Science and Engineering Research Board
Openalex Percentile: Top 52%
Remote Sensing and LiDAR Applications
0.21
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3-D Geometric-Based Indoor Stochastic Modeling Using Inverse Parabolic Scatterer Distribution — Jayanta Mukherjee, Akshay Kaushal, et al. · IEEE Transactions on Antennas and Propagation (2024) | TGRS Research Map | TGRS