Stochastic Geometry Analysis of a New GSCM with Dual Visibility Regions
Pradhan, Anish ; Dhillon, Harpreet S. ; Tufvesson, Fredrik LU
and Molisch, Andreas F.
(2023)
34th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2023
In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
- Abstract
The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent... (More)
The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.
(Less)
- author
- Pradhan, Anish
; Dhillon, Harpreet S.
; Tufvesson, Fredrik
LU
and Molisch, Andreas F.
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Channel Model, Multipath Components, Poisson Point Process, Stochastic Geometry
- host publication
- 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications : 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023 - 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023
- series title
- IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 34th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2023
- conference location
- Toronto, Canada
- conference dates
- 2023-09-05 - 2023-09-08
- external identifiers
-
- scopus:85178304875
- ISBN
- 9781665464833
- DOI
- 10.1109/PIMRC56721.2023.10293969
- language
- English
- LU publication?
- yes
- id
- 7b3f2721-93d2-4401-ab1e-e3e6e25968b1
- date added to LUP
- 2024-01-08 11:13:20
- date last changed
- 2024-01-08 11:14:34
@inproceedings{7b3f2721-93d2-4401-ab1e-e3e6e25968b1,
abstract = {{<p>The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.</p>}},
author = {{Pradhan, Anish and Dhillon, Harpreet S. and Tufvesson, Fredrik and Molisch, Andreas F.}},
booktitle = {{2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications : 6G The Next Horizon - From Connected People and Things to Connected Intelligence, PIMRC 2023}},
isbn = {{9781665464833}},
keywords = {{Channel Model; Multipath Components; Poisson Point Process; Stochastic Geometry}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC}},
title = {{Stochastic Geometry Analysis of a New GSCM with Dual Visibility Regions}},
url = {{http://dx.doi.org/10.1109/PIMRC56721.2023.10293969}},
doi = {{10.1109/PIMRC56721.2023.10293969}},
year = {{2023}},
}