Handover QoE Monitoring via Session Media Gap Analysis
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Solution Overview
Problem
Current network monitoring solutions fail to accurately detect and report the impact of handovers on subscriber Quality of Experience (QoE) in wireless communication networks, particularly in VoLTE and VoIP systems, as they do not effectively measure the media gaps associated with handover procedures, leading to poor subscriber satisfaction and increased operational costs.
Innovation Solution
A method and system for monitoring and reporting the impact of handovers on QoE by collecting session records from mobile network elements, identifying session media gaps associated with handover procedures, calculating performance metrics, and rendering interactive graphical representations of these metrics to users, thereby identifying problematic base station pairs and optimizing handover processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network monitoring solutions are used to measure handover success and network performance, then network KPIs such as handover success rate and call drop ratio can be obtained, but the impact of handovers on subscriber QoE cannot be accurately detected
Solution Approach 1:
The patent segments the media stream into multiple segments during handover procedures and identifies media gaps between segments. By analyzing these gaps, the system can detect handover-related quality degradations and associate them with specific handover events, enabling accurate QoE measurement that traditional network KPIs miss.
Solution Approach 2:
The patent introduces media gap analysis as an intermediary mechanism between network handover events and subscriber QoE perception. By measuring the gaps in media transmission during handovers, the system creates a bridge that connects network-side handover success metrics with user-side quality experience, revealing the impact information that was previously lost.
2Reliability
If handover procedures are optimized to reduce media gaps, then subscriber QoE improves, but network complexity increases due to additional monitoring and analysis requirements
Solution Approach 1:
The patent leverages existing network monitoring infrastructure and session records to perform handover analysis. By using multi-functional analysis of available data (session records, media portions, timing information), the system achieves QoE measurement without requiring entirely new dedicated monitoring hardware, thus reducing the complexity increase.
Solution Approach 2:
The system performs self-service analysis by automatically identifying media gaps, calculating performance metrics, and generating reports using data already collected by network elements. This automated approach reduces the need for manual monitoring and complex external analysis systems, thereby limiting the increase in overall system complexity while improving QoE reliability.
3Measurement precision
If detailed session records and media portions are collected and analyzed, then handover impact detection accuracy improves, but data processing requirements and operational costs increase
Solution Approach 1:
The patent extracts only the critical information needed for handover analysis from the full session records - specifically focusing on media portions, timing information, and gap identification. By extracting only these essential elements rather than processing entire session records, the system maintains high detection accuracy while significantly reducing data processing volume and operational costs.
Solution Approach 2:
The system applies partial action by analyzing only the media portions and gaps relevant to handover events rather than processing all network traffic data. This selective analysis approach provides sufficient accuracy for QoE measurement without the excessive processing requirements that would result from analyzing complete session records in detail.
4Productivity
If existing network monitoring KPIs are used, then network performance can be monitored, but actionable insights for optimizing handover procedures are not provided
Solution Approach 1:
The patent implements feedback by automatically calculating performance metrics based on detected media gaps and handover events, then generating reports that provide actionable insights. This feedback loop transforms raw monitoring data into optimized handover recommendations, enabling the system to not only monitor performance but also guide optimization efforts based on actual QoE impact.
Data Source
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AI summary
A method for monitoring, detecting and reporting the impact of handovers on the quality of experience for one or more users in a mobile cellular network includes collecting, from a plurality of mobile network elements, session records specifying data about sessions handled by two or more mobile network elements. Each session record comprises a data portion and a media portion. Each session includes two or more session segments. Session segments having session media gaps are identified from the media portions of the collected session records. At least some of the session media gaps are associated with a handover procedure between first and second mobile network elements. A plurality of performance metrics related to handover procedures in the identified session segments are calculated and stored. An interactive graphical representation of the stored performance metrics is rendered to a user via a graphical user interface.