SON Training Coordinator for Conflict-Free Network Optimization
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Solution Overview
Problem
Manual and semi-automated network management in wireless communication systems is time-consuming, error-prone, and unable to react quickly to network changes, especially in heterogeneous networks with diverse technologies, requiring automated operation, administration, and management (OAM) functions to optimize and troubleshoot complex network configurations.
Innovation Solution
A method and apparatus for self-organizing network (SON) coordination, where a training coordinator receives requests from SON functions, determines if training can be granted, and coordinates with a SON coordinator to lock network areas, avoiding conflicts by processing training requests with network-related information and events, ensuring proper training and configuration management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual and semi-automated network management is used, then network configuration and optimization can be performed with human expertise, but the process becomes time-consuming and unable to react quickly to network changes
Solution Approach 1:
The patent implements self-service through automated SON functions that can autonomously perform network management tasks including detecting network conditions, making optimization decisions, and executing configuration changes without human intervention. The system includes automated training coordination, conflict detection, and execution management that enable the network to self-manage its optimization processes.
Solution Approach 2:
The patent applies preliminary action by implementing a training coordination mechanism that schedules and prepares SON function training before actual network optimization is needed. The system proactively manages training requests, determines grant decisions based on current network state, and prepares execution plans in advance, allowing the network to respond more quickly to changes.
2Productivity
If multiple SON function instances are executed simultaneously, then network optimization coverage is improved, but conflicts arise between functions affecting the same network resources
Solution Approach 1:
The patent introduces a training coordinator as an intermediary between multiple SON function instances and the network resources they access. The coordinator receives training requests from various SON functions, determines grant decisions by evaluating conflicts with other scheduled functions, and manages the execution sequence. This intermediary layer prevents direct conflicts while maintaining high optimization coverage.
Solution Approach 2:
The patent implements dynamics by making the SON function execution schedule flexible and adaptive. The training coordinator dynamically adjusts the timing and execution of SON functions based on current network conditions, resource availability, and conflict detection results. This dynamic scheduling allows multiple functions to execute efficiently without causing instability.
3Adaptability or versatility
If SON function training is performed without coordination, then training flexibility is maintained, but network resources may be impacted by uncoordinated training activities
Solution Approach 1:
The patent implements feedback mechanisms where the training coordinator continuously monitors network resource status and SON function training progress. Based on this feedback, the coordinator can adjust training schedules, delay non-critical training activities, and prioritize functions that have higher network impact. This feedback loop maintains training flexibility while preventing harmful resource impacts.
Data Source
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AI summary
A method comprises receiving at a training coordinator training information from a self-organising network function and network related information and providing to a self-organising network function coordinator a first training request comprising a training request for said self-organising network function and one or more of network related information and training information about one or more other self-organising network functions.