Anomaly Correlation Mechanism for LTE Handover Failures
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Solution Overview
Problem
Self-organizing network (SON) based technologies face challenges in identifying and analyzing handover failures in LTE wireless communications due to changing network conditions, seasonality effects, and difficulty in identifying root causes of excessive handover failures related to radio frequency (RF) environment conditions and resource limitations in LTE evolved node Bs (eNodeBs).
Innovation Solution
A method that uses data analytics and machine learning techniques to quantify handover failures through volume and entropy metrics, identifies 'heavy-hitter' neighbor cell relations contributing to failures, and generates alerts for anomalous cell relations, enabling the automatic adjustment of neighbor relation tables to improve handover performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If self-organizing network (SON) technologies are used to automatically manage neighbor relation tables, then network automation and optimization capability are improved, but the ability to identify and analyze handover failures deteriorates due to changing network conditions and seasonality effects
Solution Approach 1:
The system performs preliminary actions by collecting and storing handover failure data, RF environment measurements, and resource status information before failures occur. This preliminary data collection enables subsequent accurate analysis despite changing network conditions and seasonality effects, resolving the contradiction between automation and analysis precision.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring handover failure patterns, comparing them against historical data and RF conditions, and automatically adjusting neighbor relation tables based on analyzed insights. This closed-loop feedback maintains both automation capability and failure analysis accuracy dynamically.
2Measurement precision
If comprehensive data collection for handover failure analysis is performed, then root cause identification capability is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The system extracts only the most relevant features from collected data, such as failure patterns, RF environment correlations, and resource status metrics, while discarding redundant information. This extraction approach maintains root cause identification accuracy while reducing system complexity and computational burden.
Solution Approach 2:
The data processing system is segmented into modular components: data collection modules, processing modules, analysis modules, and output modules. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive analysis capability through coordinated module interactions.
3Productivity
If real-time handover failure monitoring is implemented, then handover performance optimization is improved, but processing time and computational resources worsen
Solution Approach 1:
The system implements periodic monitoring and batch processing of handover failure data at optimized intervals, rather than continuous real-time processing. This periodic action maintains handover performance optimization capability while reducing computational overhead and processing time requirements.
Solution Approach 2:
The system processes only a subset of available data at each monitoring interval, focusing on critical failure patterns and significant deviations from normal operation. This partial processing approach provides timely optimization insights without requiring exhaustive analysis of all collected data, balancing speed and completeness.
Data Source
AI summary
Optimizing neighbor cell relationships for improving handover performance by interpreting handover failures is presented herein. A method can include receiving data representing a time series of failures of outgoing handovers corresponding to a wireless access point device, and determining, based on a determined condition corresponding to a metric, that a source cell of the wireless access point device is associated with an anomalous cell relation corresponding to the time series of the failures of the outgoing handovers in response to quantifying the time series based on the metric. In various examples, the quantifying can include quantifying the time series with respect to: a volume of the failures during a period of time, an entropy calculated on a probability distribution of the failures by determined relations between the source cell and target cells, and/or an entropy calculated on a probability distribution of determined outgoing handover outcomes.