SON Coordinator Framework for Resolving Mobility Load Balancing Conflicts
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Solution Overview
Problem
Modern mobile telecommunication networks face challenges in managing and troubleshooting complex issues due to incomplete and inaccurate information, leading to conflicts in Self-Organizing Network (SON) operations, particularly in parameter settings for mobility load balancing and robustness optimization.
Innovation Solution
An analytics-assisted, multi-agents, self-learning, self-managing framework that calculates compromise solutions and diagnoses based on reputations of alternative optimization and diagnostic techniques, using weighted averages or majority rules to adjust wireless configuration parameters and address performance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If multiple independent SON functions are deployed to automate network tuning, then network self-optimization capability is improved, but conflicts and unstable network behavior occur due to parameter coordination issues
Solution Approach 1:
The patent combines multiple independent SON functions (MLB and MRO) under a unified coordination framework where a central SON coordinator collects parameter suggestions from distributed SON entities, evaluates them jointly, and makes integrated decisions. This merging approach resolves conflicts by considering the interdependencies between different SON functions rather than operating them independently.
Solution Approach 2:
The patent introduces a SON coordinator as an intermediary entity that mediates between distributed SON functions. The coordinator receives parameter suggestions from multiple SON entities, resolves conflicts through coordinated decision-making, and ensures stable network behavior by considering the combined impact of different optimization actions before implementation.
2Speed
If distributed SON functions operate autonomously on individual cells, then local optimization responsiveness is improved, but parameter conflicts arise between neighboring cells
Solution Approach 1:
The patent segments the SON functionality into distributed SON entities at individual cells that operate autonomously for local optimization, while introducing a higher-level SON coordinator that handles inter-cell parameter coordination. This segmentation allows fast local responses while managing the complexity of cross-cell parameter interactions through a dedicated coordination layer.
Solution Approach 2:
The patent adds a hierarchical dimension to the SON architecture by introducing a central coordinator layer above the distributed cell-level SON entities. This dimensional change enables local cells to maintain autonomous responsiveness while the added coordination layer manages the complexity of parameter interactions across the network, resolving conflicts that span multiple cells.
3Device complexity
If traditional network management approaches are used, then system simplicity is maintained, but incomplete and inaccurate information leads to poor control decisions
Solution Approach 1:
The patent implements comprehensive feedback mechanisms where SON entities continuously monitor network performance, collect measurement data from multiple sources, and use this information to inform optimization decisions. The system gathers real-time data on network state, evaluates the effectiveness of implemented changes, and adjusts parameters based on measured outcomes, improving decision accuracy through data-driven feedback loops.
Data Source
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AI summary
The strengths of alternative self-organizing-network (SON) techniques can be leveraged by deriving a compromise result from alternative results generated by the respective SON techniques. In particular, the compromise result may be derived from the alternative results based on reputations assigned to alternative SON techniques used to generate the respective results. The compromise result may be calculated based on weighted averages of the alternative results (e.g., solutions, diagnoses, predicted values, etc. ), or on weighted averages of parameters specified by the alternative results (e.g., parameter adjustments, underlying causes, KPI values, etc. ). In such an embodiment, the weights applied to the alternative results may be based on the reputations of the corresponding SON techniques used to generate the respective alternative results.