Mobile Network Cell Clustering for Oscillation Reduction
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Solution Overview
Problem
Self-optimizing networks in mobile communication often cause oscillating effects by reconfiguring individual cell settings, which can degrade neighboring cell performance, leading to inefficient and timely issue resolution.
Innovation Solution
A method involving a coverage and capacity optimization server that classifies cell performance, clusters cells based on proximity and common issues, identifies key performance indicators, and applies remedial actions on a per-cluster basis to improve network performance, reducing oscillating effects and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual cell settings are reconfigured to improve local performance, then local cell performance is improved, but neighboring cell performance degrades
Solution Approach 1:
The patent merges multiple adjacent cells into cell clusters and applies unified remedial actions to entire clusters rather than individual cells. This clustering approach consolidates the optimization unit, ensuring that when remedial actions are applied to improve performance in one area, the entire cluster benefits collectively while maintaining coordination with neighboring clusters, thereby eliminating the oscillating effects that occur when individual cells are reconfigured in isolation.
2Productivity
If remedial actions are applied to individual cells, then specific cell issues are addressed, but systemic issues across multiple cells are not efficiently resolved
Solution Approach 1:
The patent combines multiple cells into clusters and applies remedial actions at the cluster level rather than individually. This approach increases productivity by resolving systemic issues across multiple cells simultaneously through a single optimization operation, while the automated clustering algorithm manages the complexity of identifying and treating multiple cells with similar performance characteristics.
Solution Approach 2:
The patent creates universal cluster-level remedial actions that can address multiple types of performance issues (coverage problems, capacity limitations, interference) across different cells within a cluster. This multi-functional approach allows a single optimization routine to handle various systemic issues efficiently, improving productivity while maintaining manageable complexity through standardized treatment protocols.
3Reliability
If per-cell optimization is performed, then local performance metrics improve, but overall network efficiency decreases due to oscillating effects
Solution Approach 1:
The patent merges adjacent cells into clusters and applies coordinated remedial actions at the cluster level. This approach maintains performance metric improvements across all cells in the cluster while preventing the oscillating effects that waste network energy. By treating cells as a unified optimization unit rather than isolated entities, the system achieves sustained performance improvements without the energy-wasting back-and-forth reconfigurations that occur with individual cell optimization.
Data Source
AI summary
In one embodiment, a method implemented on a computing device includes: classifying a current coverage and capacity (CCO) status according to a multiplicity of performance factors for a multiplicity of mobile network cells, clustering the mobile network cells into cell clusters based on at least the classifying and proximity of the mobile network cells to each other, based at least on the performance factors, identifying at least one problem cluster from among the cell clusters, identifying at least one underperforming master key performance indicator (MKPI) for the at least one problem cluster, and instructing the mobile network cells in the at least one problem cluster to perform at least one remedial action to address at least one of the performance factors to improve performance according to the MKPI.