Cellular Network Clustering for Traffic Pattern Prediction
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Solution Overview
Problem
Cellular networks face challenges in maintaining optimal performance due to limited network capacity and resource congestion, leading to high error rates and varying service quality across different regions, which is exacerbated by the need for extensive data collection and processing for all cells.
Innovation Solution
Implementing a network analytics system that clusters cells with similar characteristics using various clustering techniques and generates a combined forecasting model to predict network parameters, thereby conserving computing resources and improving network performance by optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network operators collect and process data for all cells to improve network performance, then network performance and service quality improve, but computing resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the cellular network into multiple clusters based on geographic proximity and network parameter similarities (traffic volume, signal strength, interference levels). Each cluster is processed independently rather than analyzing all cells globally, which reduces computing resource consumption while maintaining network performance monitoring effectiveness.
Solution Approach 2:
The patent merges cells with similar characteristics into clusters, treating them as unified groups for data collection and analysis. This consolidation reduces the total number of processing units from individual cells to cluster representatives, significantly lowering computing resource requirements while preserving the ability to detect network performance issues.
2Measurement precision
If network operators analyze data from all cells to ensure comprehensive monitoring, then service quality improves, but processing time and computational load increase
Solution Approach 1:
By dividing the network into clusters, the patent enables parallel processing of multiple clusters simultaneously, reducing overall processing time. Each cluster can be analyzed independently and concurrently, maintaining comprehensive monitoring coverage while accelerating the analysis process.
Solution Approach 2:
The patent develops a unified clustering framework that can be applied across different network types and configurations. The same clustering algorithm and analysis methods work universally for various cell densities and network topologies, providing efficient comprehensive monitoring without requiring separate processing procedures for different scenarios.
3Productivity
If cells are analyzed individually to maintain detailed network control, then network capacity optimization improves, but resource allocation efficiency decreases
Solution Approach 1:
The patent combines individual cell analyses into cluster-level analyses, where resource allocation decisions are made at the cluster level based on aggregate performance metrics. This approach maintains the ability to optimize network capacity through detailed parameter analysis while simplifying resource allocation operations by treating clusters as unified management units.
Solution Approach 2:
The patent applies different analysis and optimization strategies to different clusters based on their specific characteristics (urban vs. rural, high-traffic vs. low-traffic). Each cluster receives tailored resource allocation and optimization measures appropriate to its local conditions, improving both capacity optimization and operational efficiency compared to uniform individual cell management.
Data Source
AI summary
A device may receive an initial set of network parameter values, associated with cells of a cellular network, that are measured or calculated based on communications associated with the cells of the cellular network. The device may determine a set of feature values, associated with the cells of the cellular network, using the initial set of network parameter values. The device may cluster the cells of the cellular network into a first group of clusters using a first clustering technique, and may cluster the cells of the cellular network into a second group of clusters using a second clustering technique. The device may cluster the cells of the cellular network into a final group of clusters based on the first group of clusters and the second group of clusters, and may output information associated with the final group of clusters of the cells of the cellular network.