Wireless Network Cell Clustering by Time-Based Traffic Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless networks optimize cells individually without clustering based on time-based traffic patterns, leading to suboptimal network performance.
Innovation Solution
Implement a system with a cluster partitioning module that groups network cells into time-based clusters and an optimization module that adjusts network parameters based on recurring schedule sets defined by performance indicators, allowing for customized SON solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cells are optimized individually, then optimization simplicity is maintained, but network performance is suboptimal
Solution Approach 1:
The patent merges multiple individual cell optimizations into cluster-based optimizations. Cells with similar traffic patterns are grouped into clusters, and optimization decisions are made at the cluster level rather than individually for each cell. This reduces the overall complexity of network optimization while improving performance through coordinated resource allocation across clustered cells.
Solution Approach 2:
The patent segments the network optimization problem by dividing cells into distinct clusters based on their traffic pattern characteristics. This segmentation allows for customized optimization strategies to be applied to each cluster type (e.g., daytime clusters, nighttime clusters, weekend clusters) rather than using a single uniform optimization approach for all cells.
2Productivity
If time-based clustering is implemented, then network performance is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic cluster formation where cells are grouped into different clusters based on time-of-day traffic patterns. The system dynamically adjusts cluster memberships and optimization parameters according to recurring time-based patterns (e.g., daytime, nighttime, weekend patterns) rather than using static optimization configurations. This allows the system to adapt to changing network conditions while maintaining manageable complexity through pattern recognition.
Solution Approach 2:
The patent applies periodic optimization actions based on recurring time patterns. Instead of continuously optimizing all cells simultaneously, the system implements optimization cycles that recur at specific time intervals corresponding to typical traffic pattern changes. This periodic approach improves network performance at critical moments while reducing overall system complexity by avoiding constant optimization activities.
3Ease of manufacture
If individual cell optimization is used, then implementation simplicity is maintained, but resource allocation efficiency decreases
Solution Approach 1:
The patent combines resource allocation decisions for multiple cells into cluster-level optimizations. By merging the optimization scope from individual cells to clusters, the system achieves more efficient resource allocation through coordinated management while maintaining implementation simplicity through standardized cluster-based procedures rather than complex individual cell management.
Data Source
AI summary
In some embodiments, an apparatus includes a cluster partitioning module and an optimization module. The cluster partitioning module receives a first performance indicator set for a first instance of a time period set. The cluster partitioning module defines a recurring schedule set, where each time period from the recurring schedule set is associated with a performance indicator from the first performance indicator set and within a predefined range of a performance indicator associated with the remaining time periods from the recurring schedule set. The optimization module receives a second performance indicator set for a second instance of the time period set. The optimization module defines a metric value based on the second performance indicator set, and causes a change in a network implementation based on the metric value at each time period from a third instance of the time period set and from the recurring schedule set.