Dynamic Roaming via Automated Peer Network Outage Detection
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Solution Overview
Problem
Current wireless operators lack real-time awareness of peer operators' network status, leading to manual and inefficient decision-making on roaming agreements, especially during outages, which can strain resource capacity and impact service availability.
Innovation Solution
An automated monitoring system that identifies outage events on competitor networks by analyzing cause codes from Mobility Management Entities (MMEs) and processing data to determine significant deviations, enabling near-real-time dynamic roaming based on predefined rules and network capacity assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If operators manually monitor peer network status and make roaming decisions, then decision-making control is maintained, but response time is slow and resource strain increases during outages
Solution Approach 1:
The system performs preliminary actions by continuously monitoring peer network status and pre-identifying outage conditions before they impact service. The monitoring process tracks cause codes and network parameters in advance, so when an outage occurs, the system can immediately trigger roaming enablement without waiting for manual detection and decision-making.
Solution Approach 2:
The system enables self-service by automatically detecting peer network outages through cause code analysis and autonomously making roaming decisions based on predefined criteria. The automated process eliminates the need for manual monitoring and decision-making, allowing the system to service itself by responding to network conditions without human intervention.
2Productivity
If automated monitoring is implemented to detect peer network outages, then response speed improves, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by leveraging existing cause code data from MMEs as a mediator between network status and roaming decisions. Rather than implementing complex direct monitoring of peer networks, the system uses cause codes as an intermediate indicator that automatically signals outage conditions, simplifying the monitoring architecture while maintaining high detection efficiency.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing cause codes from MMEs and using this information to automatically adjust roaming decisions. The feedback loop processes network status data in real-time, compares it against predefined thresholds, and triggers appropriate roaming actions, creating an efficient automated system without requiring complex manual intervention structures.
3Measurement precision
If real-time cause code analysis is performed to identify outages, then detection accuracy improves, but processing requirements increase
Solution Approach 1:
The system applies parameter changes by transforming raw cause code data into meaningful outage indicators through predefined threshold comparisons. Rather than performing complex real-time analysis of all possible parameters, the system changes the state of data processing by using simple threshold-based evaluations that maintain high detection accuracy while minimizing processing resource consumption.
Solution Approach 2:
The system uses partial action by focusing analysis only on specific cause codes that are most indicative of network outages, rather than processing all possible network parameters. This selective approach achieves sufficient detection accuracy for outage identification while significantly reducing the processing burden compared to comprehensive real-time analysis of all network metrics.
Data Source
AI summary
A system enables a home operator to establish an automated monitoring process which identifies outage events on competitor wireless networks (e.g., peer operators) operating in the same geographies as home operator. The home operator then is able to selectively offer, in near real-time, to open roaming to the peer operator, or implement roaming automatically based on predefined and mutually agreed upon rule sets. The monitoring process may observe non-customer attach request volumes in order to identify outage events on competitor wireless networks.