Geospatial Forecasting for Cell Site Earnings
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Solution Overview
Problem
Deploying and maintaining communication networks is costly, and attributing network revenues across individual equipment pieces is challenging, especially when forecasting potential earnings for new network access points.
Innovation Solution
A method that uses geospatial features of existing cell sites to train prediction models, estimating the earning value of new cell sites by applying these features as inputs, and identifying geospatial categories within candidate sites based on earning values and scaling factors to determine predicted earning values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network revenues are attributed across individual equipment pieces, then revenue attribution accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces geospatial features as an intermediary layer between network equipment and revenue attribution. Instead of directly tracking revenues to individual equipment pieces, the system uses geospatial characteristics (location, coverage area, demographic data) as mediators to estimate and attribute revenues, simplifying the overall system while maintaining attribution accuracy
Solution Approach 2:
The system creates geospatial models and predictions that replicate the revenue-generating potential of cell sites without requiring direct measurement of actual revenues. These geospatial copies serve as proxies for revenue attribution, reducing the need for complex direct tracking mechanisms
2Measurement precision
If geospatial features are used to predict earning values, then forecasting accuracy improves, but data processing complexity increases
Solution Approach 1:
The system pre-calculates and stores geospatial features for cell sites before revenue attribution is needed. Geospatial characteristics such as location data, coverage metrics, and demographic information are computed in advance and stored, eliminating the need for complex real-time processing during revenue forecasting
Solution Approach 2:
The patent transforms complex geospatial data into standardized parameters and features that can be easily processed by prediction models. By converting raw geospatial information into meaningful metrics (e.g., population density, average income by location, coverage area), the system maintains high forecasting accuracy while reducing processing complexity
3Reliability
If detailed geospatial analysis is performed on candidate cell sites, then deployment decision quality improves, but computational resources required increase
Solution Approach 1:
The system performs geospatial analysis at different levels of detail depending on the deployment stage. For initial screening of candidate cell sites, only key geospatial parameters are analyzed, while detailed analysis is reserved for sites that pass preliminary evaluation. This partial approach reduces overall computational resources while maintaining decision quality
Solution Approach 2:
The patent applies different levels of geospatial analysis to different candidate locations based on their specific characteristics and deployment priorities. High-value or complex deployment sites receive more detailed analysis, while standard locations receive streamlined assessment, optimizing computational resource allocation across the network
Data Source
AI summary
A processing system may obtain usage volume information for endpoint devices for at least one cell site of a cellular network, determine at least one earning value of the at least one cell site based upon a summation of an earning metric of each of the endpoint devices for the at least one cell site, the earning metric comprising for each of the endpoint devices in a given time period: a total earning for the cellular network from the endpoint device times a ratio of the usage volume via the at least one cell site divided by the total usage volume via the cellular network, train a prediction model to predict an earning value of a new cell site, based upon geospatial features of the at least one cell site as predictor factors, and determine a predicted earning value of the new cell site via the prediction model.