Network Parameter Change Groups for De-Confused Performance Tuning
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Solution Overview
Problem
Network operators face challenges in accurately configuring carrier parameters across different locations to manage user mobility, interference, and load balancing due to the large number of network parameters and varying user and traffic behaviors, making it difficult to achieve optimal network performance.
Innovation Solution
A data-driven machine learning approach using a classifier trained on network parameter change records to identify and apply network parameter change groups based on network performance indicators, leveraging a de-confusion process to reduce impact confusion and automate optimal configuration settings across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network operators manually configure carrier parameters across different locations, then they can manage user mobility and interference, but the complexity and time required to optimize performance increases significantly due to the large number of parameters
Solution Approach 1:
The system implements self-service by automatically configuring network parameters without manual intervention. The automated system analyzes network conditions, selects appropriate parameter values, and applies configurations across multiple locations, enabling the network to self-optimize performance while reducing the burden on operators.
Solution Approach 2:
The invention systematically changes network parameters based on analyzed performance data and identified best practices. By automatically adjusting parameters such as carrier settings, handover thresholds, and interference management values, the system optimizes network performance without requiring manual parameter tuning at each location.
2Reliability
If network operators manually tune parameters for each location, then optimal performance can be achieved, but the time and resources required increase significantly
Solution Approach 1:
The system performs preliminary action by pre-analyzing network data, identifying successful parameter configurations, and preparing optimization recommendations before deployment. This advance preparation allows for rapid implementation across multiple locations, significantly reducing the time required for manual tuning while maintaining optimal performance.
Solution Approach 2:
The invention copies successful parameter configurations from one location to similar locations automatically. By identifying best practices at source locations and replicating them across the network, the system eliminates repetitive manual tuning work while ensuring consistent optimal performance across different geographical areas.
3Adaptability or versatility
If different parameter values are configured across locations to handle varying user behaviors, then network performance improves, but the difficulty of managing and maintaining consistency increases
Solution Approach 1:
The system applies local quality by configuring location-specific parameter values tailored to local network conditions, user behaviors, and traffic patterns. Each location receives customized parameters optimized for its specific characteristics, while the centralized system maintains overall consistency and coordination across the entire network.
Solution Approach 2:
The invention implements universality through a single centralized system that manages parameter configurations across all network locations. This multi-functional platform handles data collection, analysis, configuration generation, and deployment, providing a unified approach that simplifies management while accommodating local variations through automated customization.
Data Source
AI summary
A processing system may obtain a data set with records of network parameter changes, each record including at least one network parameter change and at least one attribute associated with a first aspect of a communication network, and a corresponding network performance indicator change. A first record may include a plurality of network parameter change groups. The processing system may next perform a de-confusion process by identifying a second record comprising a single network parameter change group, determining that a corresponding network performance indicator change is different from that of the at least the first record, and updating the data set to replace the first record with at least two replacement records. The processing system may apply at least one of the network parameter change groups to a second aspect of the communication network based upon a decision output of a classifier that is trained using the updated data set.