VoLTE Drop Ratio Calculation via Service Indicators
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Solution Overview
Problem
Current communication systems face challenges in accurately evaluating key performance indicators (KPIs) for double S1 and/or double next-generation (NG) releases during voice over long-term evolution (VoLTE) calls, leading to inaccurate identification of dropped calls due to vendor-specific cause information and lack of unified measurement across multiple MME vendors.
Innovation Solution
A method and apparatus that receive an initial user equipment message from a target cell, transmit an initial context setup request, and calculate a drop ratio based on abnormal radio access bearer releases to accurately determine the number of dropped connections, providing accurate KPI measurements by differentiating between normal and abnormal releases across various MME vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the MME uses vendor-specific cause information to count abnormal releases, then the release counting is simple, but the measurement precision of dropped call identification deteriorates due to inconsistent vendor interpretations
Solution Approach 1:
The patent changes the parameter being monitored from vendor-specific cause values to vendor-neutral service indicator fields. By monitoring whether service indicators are present or absent in RRC messages, the system achieves consistent dropped call identification across all MME vendors without relying on proprietary cause information interpretations.
Solution Approach 2:
The patent introduces service indicator fields as an intermediary mechanism between the MME and the performance monitoring system. These indicators act as a standardized interface that translates vendor-specific release reasons into a common language that can be universally interpreted for accurate dropped call identification.
2Productivity
If the system monitors all radio access bearer releases, then the productivity of performance evaluation is high, but the measurement precision of actual dropped calls deteriorates due to inclusion of normal releases
Solution Approach 1:
The patent extracts only the relevant subset of releases by monitoring service indicator fields that specifically indicate abnormal release conditions. This extraction approach filters out normal releases from the monitoring scope while maintaining comprehensive coverage of actual dropped calls, thus improving measurement precision without sacrificing evaluation efficiency.
Solution Approach 2:
The system uses service indicator fields as feedback mechanisms that provide real-time information about the nature of each release. By continuously monitoring these indicators, the system can dynamically distinguish between normal and abnormal releases, ensuring accurate dropped call identification while maintaining high evaluation productivity.
3Ease of manufacture
If different MME vendors use different cause value interpretations, then each vendor can optimize for their specific implementation, but the adaptability of the measurement system deteriorates due to lack of unified standards
Solution Approach 1:
The patent implements a universal monitoring approach that works across all MME vendors by focusing on service indicator fields rather than vendor-specific cause values. This universal method allows the system to adapt to different vendor implementations while maintaining consistent measurement capabilities, achieving both vendor independence and unified standards.
Solution Approach 2:
Instead of trying to harmonize vendor-specific cause interpretations (the conventional approach), the patent inverts the strategy by monitoring vendor-neutral service indicators. This inversion allows the system to work with existing vendor implementations as they are, while achieving unified measurement standards through a different observation angle.
Data Source
AI summary
An apparatus comprising at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus to receive an initial user equipment message from a target cell, transmit an initial context setup request message to the target cell including one or more service indicators, receive an initial context setup response message from the target cell, and calculate a drop ratio associated with a double S1 connection based upon a number of abnormal radio access bearer releases subtracted from a number of radio access bearer releases associated with the successful double S1 connection divided by a total number of radio access bearer releases. The one or more service indicators are determined based upon the received initial user equipment message.