Wireless Signal Session Data Analysis for Coverage Bottlenecks
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Solution Overview
Problem
Users of wireless devices often experience frustration and loss of productivity due to poor or failed wireless signal access in areas with high usage or physical obstructions, as existing wireless systems lack effective methods to detect and report on wireless signal performance metrics and patterns within geographical areas.
Innovation Solution
A method and system that analyze wireless signal session data records to detect predefined relationships and patterns in wireless signal performance metrics within a geographical area, generating reports that can be used by wireless system resources to improve service quality and user advisories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless systems operate in areas with high usage or physical obstructions, then wireless signal access is denied or degraded, but increasing infrastructure coverage to address this results in finite geographic coverage and operational limitations
Solution Approach 1:
The system performs preliminary analysis of wireless session data records to detect patterns and relationships that indicate potential signal issues before they affect users. By analyzing historical data and detecting predefined relationships in advance, the system can identify areas prone to signal degradation and take preventive measures, such as adjusting network parameters or alerting users before entering problematic zones.
Solution Approach 2:
The system continuously monitors wireless session data and provides feedback through generated reports that identify patterns in signal performance. This feedback loop enables the wireless system to adjust operations based on detected relationships, improving reliability in high-usage or obstructed areas by learning from historical performance data and adapting network behavior accordingly.
2Adaptability or versatility
If wireless systems provide comprehensive coverage, then more areas are served, but signal access fails in areas with high system usage or physical obstructions like large structures
Solution Approach 1:
The system analyzes wireless session data records in advance to detect predefined relationships that indicate signal problems caused by physical obstructions or high usage. By identifying these patterns before they manifest as user complaints, the system can adapt its coverage strategy proactively, adjusting network parameters or routing signals around detected problem areas.
Solution Approach 2:
The system uses feedback from analyzed session data to continuously improve its understanding of coverage limitations. By detecting relationships between signal performance and environmental factors (such as large structures or traffic patterns), the system adapts its operation to maintain reliability across diverse geographic areas, learning from each detected pattern to improve future coverage decisions.
3Loss of information
If no analysis method is implemented, then system complexity remains low, but wireless signal performance issues and patterns remain undetected and unreported
Solution Approach 1:
The system segments the complex task of wireless signal analysis by detecting predefined relationships in session data records. Rather than attempting to analyze all possible signal parameters simultaneously, the system divides the problem into specific, pre-defined relationship patterns (such as signal strength thresholds, time-based patterns, or location-based correlations), making the analysis manageable and systematic.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes wireless session data records to detect predefined relationships. This intermediary layer acts as a mediator between raw signal data and actionable insights, transforming complex raw data into structured information about signal performance patterns without requiring direct complex processing of all underlying signal parameters.
4Productivity
If wireless users access services in high-usage areas, then service utilization increases, but users experience frustration and loss of productivity due to poor signal access
Solution Approach 1:
The system provides feedback to users and network operators about detected patterns in signal performance during high-usage periods. By analyzing session data and identifying relationships between usage patterns and signal quality, the system can alert users to potential access problems before they occur or provide alternative service recommendations, maintaining productivity even in high-usage areas.
Solution Approach 2:
The system performs preliminary detection of usage patterns and signal performance relationships to identify areas where high utilization may lead to poor access reliability. By detecting these patterns in advance through analysis of session data, the system can take preliminary actions such as load balancing, user notification, or network parameter adjustment to prevent productivity loss before it occurs.
Data Source
AI summary
Example methods disclosed herein include analyzing session records associated with a wireless system to determine a time period between an access of a first wireless system resource by a wireless device and an access of a second wireless system resource by the wireless device. Example methods disclosed herein also include determining a rate at which the wireless device is traversing through a coverage area of the wireless system, the rate being determined based on the time period. Example methods disclosed herein further include generating a report including the rate.