Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.16827 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908668426977280 |
|---|---|
| author | Modad, Bassel Abou Ali Yu, Xin Chiang, Yao-Yi Molisch, Andreas F. |
| author_facet | Modad, Bassel Abou Ali Yu, Xin Chiang, Yao-Yi Molisch, Andreas F. |
| contents | Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread, etc.. Existing models, although useful, are based on very limited datasets. In this paper, we establish a framework to produce high accuracy LOS models from geospatial data in different environments, and apply it to create a LOS model for macrocells, using datasets of the United States (US) on a nationalscale, using more than 13, 000 locations of real-world macrocells. Based on this we create a new, fully parameterized model that better describes macrocell deployments in the US than the 3GPP model. We furthermore demonstrate that for improved accuracy the LOS probability should be modeled on a per cell basis, and the model parameters treated as random variables; we provide a full description and parameterization of this novel approach and by simulations show that it better predicts the inter-cell interference at the cell-edge than an average-based model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Line-of-Sight Probability in Macrocells: Framework, Statistical Models, and Parametrization from Massive Real World Datasets in the USA Modad, Bassel Abou Ali Yu, Xin Chiang, Yao-Yi Molisch, Andreas F. Signal Processing Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread, etc.. Existing models, although useful, are based on very limited datasets. In this paper, we establish a framework to produce high accuracy LOS models from geospatial data in different environments, and apply it to create a LOS model for macrocells, using datasets of the United States (US) on a nationalscale, using more than 13, 000 locations of real-world macrocells. Based on this we create a new, fully parameterized model that better describes macrocell deployments in the US than the 3GPP model. We furthermore demonstrate that for improved accuracy the LOS probability should be modeled on a per cell basis, and the model parameters treated as random variables; we provide a full description and parameterization of this novel approach and by simulations show that it better predicts the inter-cell interference at the cell-edge than an average-based model. |
| title | Line-of-Sight Probability in Macrocells: Framework, Statistical Models, and Parametrization from Massive Real World Datasets in the USA |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.16827 |