3D UAV Network Coverage Modeling for Altitude-Robust Flight Paths
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Solution Overview
Problem
Current wireless communication network coverage models are limited to 2-dimensional representations, which fail to accurately predict network robustness in vertically layered 3-dimensional sections of airspace, particularly affecting unmanned aerial vehicle (UAV) operations by not accounting for varying altitudes and environmental factors.
Innovation Solution
A 3-dimensional network coverage modeling technique using a coverage forecast engine that generates models based on network configuration and environmental data, incorporating machine learning algorithms to predict signal robustness across vertically layered sections, allowing for flight path modifications to maintain minimum network coverage thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2-dimensional network coverage models are used, then the model complexity is low and easy to implement, but the measurement precision of network coverage prediction in 3-dimensional airspace is insufficient
Solution Approach 1:
The patent transitions from traditional 2-dimensional network coverage models to 3-dimensional coverage models that incorporate altitude as an additional dimension. This enables accurate prediction of network coverage in vertically layered airspace sections, addressing the limitations of flat ground-level models for UAV operations at different altitudes.
Solution Approach 2:
The 3-dimensional airspace is divided into multiple vertically layered sections, each with its own coverage characteristics. This segmentation allows the system to model and predict network coverage independently for each altitude layer, improving prediction accuracy while managing complexity through modular analysis.
2Measurement precision
If 3-dimensional network coverage modeling is implemented, then the network coverage prediction accuracy for UAV operations is improved, but the computational resources and processing time increase
Solution Approach 1:
The system pre-computes and stores network coverage characteristics for each vertically layered 3-dimensional section before UAV operations begin. This preliminary modeling allows rapid query and prediction during actual UAV flights, reducing real-time computational requirements and processing time.
3Adaptability or versatility
If traditional 2-dimensional coverage models are used, then the ease of operation is high, but the adaptability to varying altitudes and environmental factors is poor
Solution Approach 1:
The patent extends coverage modeling from 2-dimensional ground planes to 3-dimensional space by incorporating altitude as the third dimension. This enables the model to adapt to UAV operations at varying altitudes and different environmental conditions in vertically layered airspace, while maintaining systematic implementation through structured data organization.
Data Source
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AI summary
A coverage model is generated to forecast network coverage robustness for vertically layered 3 dimensional sections of airspace above an area. Network configuration data for multiple base stations of a wireless communication network located in an area are received. Environmental data that includes information on natural and manmade features in the area are received. The airspace above the area into a plurality of vertically layered 3-dimensional sections is segregated. A coverage model is generated based at least on the network configuration data and the environmental data for predicting network coverage of the wireless communication network in the 3-dimensional sections in the airspace above the area.