Wireless Network Mobility Robustness Optimization via KPI Deviation Analysis
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Solution Overview
Problem
Current wireless communication networks face challenges in optimizing mobility performance while maintaining data transfer efficiency, particularly due to manual configuration of handover parameters leading to radio link failures and inefficient resource use.
Innovation Solution
The implementation of a system that dynamically monitors and modifies mobility parameters using multiple key performance indicators (KPIs) to optimize handover execution, reduce radio link failures, and improve downlink data throughput by applying well-defined mobility robustness optimization changes to specific regions of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of handover parameters is used, then network reliability can be improved through optimized settings, but the complexity and time consumption of configuration increases significantly
Solution Approach 1:
The system automatically monitors multiple KPIs and deviations therefrom to identify performance issues and triggers self-optimization by automatically adjusting handover parameters without requiring manual intervention. The optimization system performs self-diagnosis and self-tuning based on real-time network conditions, eliminating the need for complex manual configuration while maintaining high reliability.
Solution Approach 2:
The system continuously monitors multiple KPIs (handover success rate, radio link failures, data throughput) and uses this feedback to dynamically adjust handover parameters. The deviation analysis provides real-time feedback loops that automatically optimize network performance based on actual measurements, replacing manual configuration with automated closed-loop control.
2Measurement precision
If multiple KPIs are monitored to optimize network performance, then optimization accuracy improves, but the complexity of monitoring and analysis increases
Solution Approach 1:
The monitoring system is segmented into modular components that track individual KPIs (handover success rate, radio link failures, data throughput) separately before aggregating them for comprehensive analysis. This segmentation allows precise measurement of each parameter while simplifying the overall system architecture through standardized monitoring modules that can be independently configured and analyzed.
3Reliability
If handover parameters are manually optimized, then network robustness improves, but the time required for optimization increases
Solution Approach 1:
The system performs preliminary monitoring and analysis of network conditions before triggering optimization actions. By continuously collecting KPI data and identifying potential issues in advance, the system can proactively adjust handover parameters before problems occur, reducing the time needed for reactive optimization and improving overall network robustness.
Solution Approach 2:
The optimization system operates continuously, constantly monitoring KPIs and deviations to maintain optimal network performance at all times. This continuous operation eliminates the need for periodic manual optimization cycles, ensuring that network robustness is maintained through uninterrupted automated tuning rather than intermittent manual intervention.
Data Source
AI summary
A system and method for dynamically improving or optimizing the performance and robustness of a wireless communication network such as a mobile communication system or cellular telephony network are disclosed. In some aspects, a plurality of time and space dependent key performance indicators (KPI) are used as part of a statistical determination of a pattern and schedule for optimizing the design, configuration and operation of the network. By dynamically applying a method of multiple KPI deviations (MKD) the system and method improves handover execution in cellular or similar systems and reduces radio link failures and improves overall subscriber service quality.