Wireless Access Point Partitioning for Cluster Management
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Solution Overview
Problem
Current network management methods for wireless access points are inefficient due to dense deployments leading to contention and interference, particularly in residential/urban areas, as they rely on individual optimization and are not effective in forming disjoint management clusters that account for actual signal correlations between access points.
Innovation Solution
A partitioning method that forms management clusters based on neighbor information and centrality parameters, where access points with higher influence are prioritized as primary members, and neighboring access points with lower centrality are added as secondary members, ensuring that access points within a cluster have a high dependency and correlation, while those between clusters operate independently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual optimization algorithms are applied to each access point independently, then each network can be optimized on an individual basis, but frequent algorithm triggering occurs due to high correlation between networks leading to a domino effect
Solution Approach 1:
The patent merges multiple highly correlated access point networks into clusters that are optimized together as a single unit. By combining networks with high correlation coefficients into unified optimization groups, the system reduces the frequency of algorithm triggering while maintaining individual network optimization capabilities, thereby resolving the domino effect problem.
Solution Approach 2:
The patent segments the overall network into distinct clusters based on correlation analysis. Each cluster represents a group of access points with high mutual correlation, allowing optimization to be performed at the cluster level rather than individually for each access point, thus reducing redundant algorithm triggering.
2Device complexity
If access points are managed individually, then each access point can be optimized independently, but network performance deteriorates due to contention and interference in dense deployments
Solution Approach 1:
The patent combines access points into clusters based on spatial proximity and signal correlation, enabling coordinated optimization that accounts for inter-network dependencies. This merging approach reduces contention and interference by considering the collective behavior of correlated access points, thereby improving network performance while maintaining manageable cluster sizes.
Solution Approach 2:
The patent applies local quality optimization by forming clusters with heterogeneous characteristics - each cluster contains access points with specific correlation patterns and deployment characteristics. This allows tailored optimization strategies for each cluster based on its local network conditions, rather than applying uniform individual optimization.
3Productivity
If Wi-Fi SON clusters are formed, then optimization can be performed in a distributed manner improving efficiency, but the challenge arises in deriving disjoint sets of access points that represent proper management clusters
Solution Approach 1:
The patent performs preliminary correlation analysis and spatial assessment to pre-identify candidate clusters before optimization is executed. By calculating correlation coefficients and assessing spatial relationships in advance, the system prepares disjoint set partitions that can be directly used for distributed optimization, reducing the complexity of real-time cluster derivation.
Solution Approach 2:
The patent enables access points to self-organize into clusters based on their measured signal correlations and spatial relationships. Each access point contributes to the correlation matrix and cluster formation process, allowing the system to automatically derive disjoint sets without complex external intervention, thereby simplifying the cluster derivation process while maintaining high optimization efficiency.
Data Source
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AI summary
A partitioning method for partitioning wireless access points into management clusters of access points, wherein each management cluster of management clusters comprises at least one access point of access points, the partitioning method comprising: obtaining from each access point of access points neighbour information comprising at least: an access point identifier of each neighbouring access point which is sensed by said respective access point; and a signal strength indicator corresponding to said sensed neighbouring access point; determining for each access point of access points a centrality parameter based on the obtained neighbour information, wherein the centrality parameter is representative for an amount of influence the respective access point has within access points; and partitioning access points into a first management cluster and at least one further management cluster by: forming the first management cluster by including in said first management cluster a first access point as primary member, said first access point having the highest centrality parameter from among access points, and any neighbouring access point sensed by said first access point as secondary member; and for each further access point of points except the first access point, in order of descending centrality parameter, forming a further management cluster by including in said further management cluster said respective further access point as primary member and, as secondary member, any neighbouring access point sensed by said respective further access point which has not been included as secondary member in another management cluster.