VoLTE Voice Quality Fault Localization via Segmented KPI Analysis
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Solution Overview
Problem
Existing wireless systems using VoLTE do not effectively identify the sources of quality degradation in voice calls, lacking the ability to analyze quality issues at higher temporal granularities and provide insights into specific causes of quality problems.
Innovation Solution
A method and network node that compute location-specific KPIs along the voice call path, using a set of predefined rules to evaluate performance criteria and identify sources of quality degradation, including UEs, gateway domains, and radio access networks, allowing for more granular analysis of voice call quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing VoLTE systems monitor voice call performance using KPIs sampled by probes, then overall call performance insight is obtained, but the ability to identify specific sources of quality degradation and perform analysis at higher temporal granularities is lacking
Solution Approach 1:
The patent segments the voice call path into multiple distinct domains (UE, radio access network, gateway domain, core network) and computes KPIs at each segment boundary. This segmentation enables precise identification of which specific domain causes quality degradation, transforming the inability to locate problems into the ability to pinpoint exact sources of issues.
Solution Approach 2:
The patent introduces a network node as an intermediary that collects KPI data from multiple probes positioned at different locations along the voice call path. This intermediary correlates KPI values from various domains and applies evaluation rules to determine the source of quality degradation, enabling centralized analysis without requiring complex distributed intelligence at each probe.
2Loss of time
If probes compute KPI values from observed characteristics, then overall call performance is analyzed, but analysis at higher levels of temporal granularity is not achieved
Solution Approach 1:
The patent performs preliminary computation of location-specific KPIs at each probe before central correlation. Each probe pre-processes its observed characteristics into meaningful KPI values (such as packet loss, jitter, delay) that can be directly correlated by the network node, reducing the complexity of real-time analysis while preserving temporal granularity.
Solution Approach 2:
The system establishes feedback loops where KPI data flows from probes to the network node for correlation and analysis. The network node continuously monitors KPI values and can trigger detailed diagnostics when quality degradation is detected, creating a feedback mechanism that maintains high temporal granularity while managing information processing efficiently.
3Reliability
If existing systems provide insight into overall call performance, then quality problems can be detected, but specific causes of quality degradation cannot be identified
Solution Approach 1:
The patent applies local quality by computing KPIs specific to each domain (UE, radio access network, gateway, core network) rather than a single overall KPI. Each domain's KPIs reflect local performance characteristics, enabling the system to detect quality problems and simultaneously identify which specific domain is the source of degradation through localized measurement.
Solution Approach 2:
The patent adds a spatial dimension to quality monitoring by measuring KPIs at multiple locations along the voice call path. Instead of a single overall quality metric, the system creates a multi-dimensional view of quality across different domains and time points, allowing simultaneous detection of quality problems and identification of their specific sources through correlation analysis.
Data Source
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AI summary
Methods and apparatus for identifying a source of quality degradation for a media call flowing in a first direction and a second direction between a mobile-originating user equipment (UE) and a mobile-terminating (MT) UE are presented. In an example method, a network node obtains a set of multiple rules specified respectively for multiple candidate sources of quality degradation for the media call in the first direction. These rules indicate whether a particular candidate source is an actual source of quality degradation based on values of certain key performance indicators (KPIs). The network node identifies, from among the multiple candidate sources of quality degradation, one or more sources of quality degradation by evaluating one or more rules from the set of multiple rules.