SON Framework Using KPIs for Automated Network Optimization
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Solution Overview
Problem
Current Self-Organizing Network (SON) applications lack a comprehensive framework to quantify and optimize network performance using key performance indicators (KPIs), leading to inefficiencies in parameter configuration and fault detection, which affects service quality and operational costs.
Innovation Solution
A novel SON framework that utilizes KPIs to quantify network performance, predict automation opportunities, and optimize operations by employing an A3 engine with data stream, determination, KPI, and automation modules to analyze network data, detect anomalies, and automate parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration and optimization of network parameters is performed, then network performance can be adjusted, but operational costs and time consumption increase
Solution Approach 1:
The system implements self-service through automated SON applications that independently perform network parameter configuration, optimization, and fault detection without requiring manual intervention. The A3 engine with its data stream, determination, KPI, and automation modules enables the network to self-diagnose and self-optimize, eliminating the need for continuous manual management while maintaining high performance standards
Solution Approach 2:
The system performs preliminary actions by pre-configuring network parameters and optimization rules before actual network operations begin. The framework establishes KPI thresholds, automation triggers, and decision-making algorithms in advance, allowing the SON applications to immediately respond to network conditions without requiring real-time human analysis or configuration decisions
2Measurement precision
If comprehensive network monitoring and analysis is implemented, then fault detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex network monitoring task into distinct functional modules: data stream collection, determination logic, KPI calculation, and automation execution. Each module handles a specific aspect of network analysis, making the overall system more manageable and maintainable while achieving comprehensive monitoring through the coordinated operation of these specialized components
Solution Approach 2:
The framework introduces intermediary elements including standardized KPI metrics that translate complex network states into measurable indicators, and automated decision rules that mediate between raw data and actionable insights. These intermediaries simplify the relationship between network conditions and response actions, reducing the apparent complexity while improving detection accuracy
3Ease of operation
If automated SON applications are deployed, then operational costs reduce, but implementation complexity and initial investment increase
Solution Approach 1:
The SON framework implements universality by designing a multi-functional automation platform that can handle diverse network optimization tasks, fault detection scenarios, and parameter adjustment operations through a single integrated system. The modular architecture allows the same core infrastructure to serve multiple purposes, reducing the need for separate specialized systems and justifying the initial investment through broad applicability
Data Source
AI summary
Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON's application effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network.