Network Expansion Optimization Using UE Data
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Solution Overview
Problem
Current methods for network expansion require significant human and resource allocation, relying on outdated Census data and labor-intensive drive tests to determine optimal locations for new access points.
Innovation Solution
The use of computerized algorithms that process UE data collected from multiple user equipment devices to optimize network expansion, eliminating the need for traditional drive tests and leveraging real-time data for more accurate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional drive tests with employees are used to collect network coverage data, then data can be collected at specific geographic locations, but significant human and resource allocation is required
Solution Approach 1:
The patent applies self-service by enabling UEs to automatically collect and report network coverage data without human intervention. The UEs perform measurements and transmit data to the network carrier autonomously, eliminating the need for employees to conduct drive tests while maintaining data collection accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual drive tests with an automated electronic data collection system. Instead of employees physically driving through geographic locations to collect data, the system uses UEs to automatically gather and transmit network coverage information, substituting human labor with automated technology.
2Ease of manufacture
If Census data is used for network expansion decisions, then data access is simplified, but the data may be outdated and unreliable
Solution Approach 1:
The patent implements feedback by continuously collecting real-time network coverage data from UEs and using this information to update network expansion decisions. This creates a closed-loop system where current operational data feeds back into planning processes, ensuring decisions are based on up-to-date information rather than outdated Census data.
Solution Approach 2:
The patent applies preliminary action by proactively collecting and analyzing network coverage data from UEs before making network expansion decisions. This allows the network carrier to identify coverage gaps and optimize access point locations based on actual current conditions, rather than relying on pre-existing outdated demographic data.
3Measurement precision
If manual drive tests are conducted to identify coverage gaps, then specific geographic locations can be assessed, but the process is labor-intensive and time-consuming
Solution Approach 1:
The patent enables UEs to automatically perform coverage measurements and report data without human intervention. This self-service approach allows continuous data collection across multiple geographic locations simultaneously, eliminating the time required for manual drive tests while maintaining accurate coverage gap identification.
Solution Approach 2:
The patent collects network coverage data from UEs in advance of network expansion decisions, allowing the network carrier to identify coverage gaps and plan access point deployments proactively. This preliminary data collection eliminates the need for time-consuming on-site assessments at later stages.
Data Source
AI summary
Systems and methods are provided herein to dynamically and efficiently optimize network expansion of a network carrier based on UE data collected from a plurality of user equipment (UEs). The UE data is processed based on different computerized algorithms to locate optimal locations for adding a new access point to expand the network. Aspects herein are also directed to systems and methods for generating and displaying dynamic maps on graphical user interfaces (GUI) in accordance with aspects herein.