Summary of Machine Learning-based Path Loss Modeling with Simplified Features, by Jonathan Ethier and Mathieu Chateauvert
Machine Learning-Based Path Loss Modeling with Simplified Features
by Jonathan Ethier, Mathieu Chateauvert
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel approach uses environmental information to improve wireless signal propagation modeling by leveraging simplified scalar features involving total obstruction depth along the direct path. This streamlines complex models, offering an accurate and practical solution for efficient wireless network planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless signals need to be accurately predicted to work well. A new way of doing this is being proposed that uses information about the environment to help make predictions. Instead of using complicated models, it’s suggested that we use simpler features like how much obstacles there are on the path from transmitter to receiver. This might sound simple, but surprisingly it can provide a good solution for planning wireless networks. |
