RF Spectrum Propagation Modeling with ML-Guided Ray Tracing
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Solution Overview
Problem
Conventional ray tracing for RF signal propagation modeling is computationally expensive and inefficient for large, complex wireless networks, making it infeasible to optimize RF signal propagation quickly and resource-intensively.
Innovation Solution
A hybrid approach combining deep learning with ray tracing, where deep learning models predict photon emission directions for select coverage pivots, reducing the need for extensive ray tracing simulations by identifying optimal emission angles and budgets, thus optimizing signal propagation modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ray tracing is used for RF signal propagation modeling, then accurate signal coverage predictions are achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the coverage area into discrete grid cells and identifies specific coverage pivots within each cell. Instead of performing ray tracing for all possible locations and directions, the system focuses computational efforts on these segmented pivot points, reducing the overall computational burden while maintaining prediction accuracy across the entire coverage area.
Solution Approach 2:
The patent performs preliminary identification of coverage pivots and photon emission directions before executing the full ray tracing simulation. By pre-determining which locations and angles are most critical for signal propagation, the system prepares a reduced set of simulation targets, thereby improving modeling efficiency without sacrificing accuracy.
2Reliability
If extensive ray tracing simulations are performed to cover all directions and locations, then complete signal propagation coverage is achieved, but computational cost and time increase significantly
Solution Approach 1:
The patent applies partial action by performing ray tracing only for selected coverage pivots and specific photon emission directions rather than exhaustive coverage of all possible directions and locations. This selective approach maintains reliable signal propagation coverage for critical areas while significantly reducing the time and computational resources required.
Solution Approach 2:
The patent applies local quality by concentrating computational resources on specific high-priority coverage pivots and emission directions that have the greatest impact on signal propagation. Instead of uniform treatment of all directions, the system identifies and focuses on locally critical areas, achieving reliable coverage predictions more efficiently.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining data regarding an environment associated with a cell site, identifying one or more coverage pivots from a plurality of coverage locations in the environment, utilizing one or more machine learning (ML) models to determine emission directions for the one or more coverage pivots based at least in part on locations of the one or more coverage pivots and the cell site in the environment, resulting in determined emission directions, and causing ray tracing simulation to be performed using the determined emission directions, wherein the ray tracing simulation enables first signal coverage estimates to be derived for the one or more coverage pivots and used to extrapolate second signal coverage estimates for a remainder of the plurality of coverage locations. Other embodiments are disclosed.