Prediction Model for Airborne Network Coverage from Ground Data
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Solution Overview
Problem
Current methods for determining mobile telecommunications network coverage above ground level are limited by the scarcity of measurements, making it expensive and time-consuming to assess coverage over a wide geographic range, especially for airborne devices like drones.
Innovation Solution
A computer-implemented method using ground-based measurements to train a prediction model that estimates network coverage at above-ground locations by selecting a subset of ground-based data points relative to the airborne location, allowing for accurate coverage determination without extensive airborne measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated flights of airborne devices are made to directly measure network coverage above ground level, then measurement precision is improved, but loss of time and productivity deteriorate due to expensive and time-consuming flights over limited geographic ranges
Solution Approach 1:
The system performs preliminary measurements at ground-based locations before conducting airborne measurements. These ground-based measurements are used to train a prediction model that can estimate above-ground coverage, reducing the need for extensive dedicated flights while maintaining measurement accuracy.
Solution Approach 2:
A prediction model acts as an intermediary between ground-based measurements and above-ground coverage determination. The model learns the relationship between ground and airborne measurements and uses this knowledge to predict above-ground coverage from ground-based data, eliminating the need for direct airborne measurements in all locations.
2Measurement precision
If dedicated flights are conducted to measure network coverage above ground level, then measurement precision is improved, but productivity deteriorates due to limited geographic range coverage
Solution Approach 1:
The prediction model serves multiple functions: it predicts network coverage above ground level, identifies geographic regions with reliable coverage, and supports drone operation planning. This multi-functionality increases productivity by eliminating the need for separate measurement campaigns for different purposes.
Solution Approach 2:
The system creates a virtual representation (copy) of above-ground network coverage based on ground-based measurements. This copied coverage information can be used for planning and analysis without requiring physical airborne measurements, significantly improving geographic range coverage efficiency.
3Productivity
If ground-based measurements are used to determine above ground coverage, then productivity is improved by reducing dedicated flights, but measurement precision may deteriorate due to insufficient above ground measurements
Solution Approach 1:
The system performs preliminary ground-based measurements and uses them to train a prediction model before determining above-ground coverage. This preliminary training phase ensures that the model learns accurate relationships between ground and airborne measurements, maintaining precision while improving productivity during actual coverage determination.
Solution Approach 2:
The system uses ground-based measurements as feedback to continuously improve the prediction model. By comparing model predictions with actual ground-based measurement data, the system refines its estimates of above-ground coverage, maintaining measurement precision while avoiding the need for extensive dedicated flights.
Data Source
AI summary
A method of determining above ground coverage of a mobile telecommunications network. Data is received which is representative of coverage of the mobile telecommunications network at a plurality of ground based locations. A location above ground is identified for which coverage of the mobile telecommunications network is to be determined. A subset of the data representative of coverage at the plurality of ground based locations is selected, where the subset comprises data representative of coverage of the mobile telecommunications network at a subset of the ground based locations. Selecting the subset comprises selecting the subset of the ground based locations in dependence on their location relative to the identified location above ground. A property of the identified location above ground and the selected subset of data representative of coverage at ground based locations are provided as inputs to a prediction model. The prediction model is configured through training, to determine coverage of a mobile telecommunications network at locations above ground in dependence on data representative of coverage of the mobile telecommunications network at one or more ground based locations. The prediction model is implemented to generate an output in dependence on the provided inputs, wherein the output of the model is representative of the coverage of the mobile telecommunications network at the identified location above ground.