Machine Learning-Based Path Loss Modeling with Simplified Features
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909212571860992 |
|---|---|
| author | Ethier, Jonathan Chateauvert, Mathieu |
| author_facet | Ethier, Jonathan Chateauvert, Mathieu |
| contents | Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based Path Loss Modeling with Simplified Features Ethier, Jonathan Chateauvert, Mathieu Machine Learning Networking and Internet Architecture Systems and Control Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that detailed knowledge of the physical environment (terrain and clutter) is essential, we propose a novel approach that uses environmental information for predictions. Instead of relying on complex, detail-intensive models, we explore the use of simplified scalar features involving the total obstruction depth along the direct path from transmitter to receiver. Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation, providing a practical solution for efficient and effective wireless network planning. |
| title | Machine Learning-Based Path Loss Modeling with Simplified Features |
| topic | Machine Learning Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2405.10006 |