Wi-Fi Network Slicer for Scalable RRM Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Wi-Fi RRM solutions lack scalability, leading to inefficiencies in large networks due to excessive RRM configuration changes, which result in high signaling and processing overhead, and are often not vendor-agnostic, disrupting network dynamics and customer services.
Innovation Solution
Implementing a network slicer that partitions the Wi-Fi network into smaller, independent segments based on RF scan data from access points, using clustering and segmentation algorithms to minimize RF interference and support tens of millions of access points, allowing for timely RRM optimization and flexible deployment across various network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized RRM solution is implemented to optimize network-wide performance, then network optimization quality improves, but signaling overhead and processing complexity increase
Solution Approach 1:
The patent divides the large-scale Wi-Fi network into multiple smaller clusters, each managed by a local RRM controller. This segmentation allows localized optimization decisions to be made independently, reducing the signaling overhead to and from a central controller while maintaining effective network-wide optimization. Each cluster operates autonomously within its domain, eliminating the need for centralized coordination of every RRM parameter change.
2Productivity
If RRM configuration changes are made frequently to optimize performance, then network performance improves, but signaling overhead and disruption to customer services increase
Solution Approach 1:
The patent implements local RRM controllers that make optimization decisions based on local cluster conditions rather than network-wide changes. This allows performance optimization to occur locally without triggering extensive signaling across the entire network. Each cluster can adjust its parameters independently based on local traffic patterns and interference conditions, reducing both signaling overhead and disruption to customer services.
Solution Approach 2:
The system performs preliminary analysis of RRM parameter changes within local clusters before implementation. By evaluating the impact of potential configuration changes locally first, the system can optimize performance while avoiding changes that would cause excessive signaling or service disruption. This preliminary assessment allows for more controlled and targeted optimization actions.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method and node are disclosed. According to one aspect, a method includes receiving scan reports from a plurality of access points, AP, and partitioning a wireless communication network into clusters of APs for which scan reports are received, the APs being partitioned into clusters based on neighbor relationships, there being a first limit to a number of APs that can be in a cluster. In some embodiments, the node is a manager node such as a self-organizing network (SON) manager node.