Abstract
Traditional techniques for estimating WiFi coverage and capacity rely on manual surveys and measurements, which are costly, time consuming, labor intensive, and do not scale with the region under coverage. This disclosure describes techniques that leverage machine learning to predict the coverage and capacity of a WiFi network given a building floor plan, placement and configuration of access points within the floor plan, and the distribution of users within the region of coverage. Existing floorplans (including placement of building materials, e.g., concrete, steel, wood, drywall, etc.), seating data, and coverage/capacity maps are used as data to train a machine learning (ML) model. The trained ML model is used to rapidly estimate coverage and capacity given a new floor plan and network configuration and people distribution within the physical region described by the floor plan.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
n/a, "Estimating WiFi Coverage with Artificial Intelligence", Technical Disclosure Commons, (May 09, 2025)
https://www.tdcommons.org/dpubs_series/8108