Hub Location Estimation Using Attribute-Specific Cloud Data
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Solution Overview
Problem
Network operators and service providers face challenges in accurately determining the locations of cell towers and other hubs due to outdated records, difficulty in obtaining competitor information, and varying distance tolerances based on attributes such as RAN technology and customer types, which affect estimation accuracy.
Innovation Solution
A method involving the separation of datasets into attribute-specific subsets, ordering these subsets by maximum supported distance relative to a hub, and generating location estimates using cloud-sourced data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-sourced data is used to estimate hub locations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the dataset into multiple attribute-specific subsets (e.g., RAN technology type, customer type) before processing. This segmentation allows the system to handle complex data by breaking it down into manageable portions, each processed according to its specific attributes, thereby improving location estimation accuracy while maintaining system manageability
Solution Approach 2:
The patent introduces a new dimension of analysis by ordering subsets based on maximum supported distance parameters and using multiple data sources (cloud-sourced data, network logs, geospatial data). This dimensional approach transforms the problem from simple location estimation to a multi-parameter optimization problem, improving precision through comprehensive data utilization
2Measurement precision
If attribute-specific subsets are processed separately, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-segregating the dataset into attribute-specific subsets and pre-ordering them by maximum supported distance before the main processing begins. This preliminary organization allows the system to efficiently process data during execution without time-consuming sorting or filtering operations, thus reducing overall processing time while maintaining high precision
Solution Approach 2:
The patent implements dynamic processing where the system adjusts its processing strategy based on the ordered subsets and their associated maximum supported distance parameters. By dynamically selecting which subsets to process first or prioritize, the system optimizes processing time while maintaining measurement precision across different attribute types
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a dataset, the dataset including first data pertaining to geographical information associated with a plurality of entities, separating the dataset into a plurality of subsets, wherein each subset of the plurality of subsets corresponds to at least one attribute of a plurality of different attributes, ordering the plurality of subsets in accordance with respective parameter values associated with each of the subsets, wherein each of the respective parameter values corresponds to a supported distance relative to a hub, and based on the ordering, generating an estimate of a location of the hub. Other embodiments are disclosed.