Cell Clustering for Cellular Network Modeling
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Solution Overview
Problem
Current modeling techniques for cellular networks require extensive computational resources and time to accurately predict and update demand and performance, especially in large and complex networks like LTE and 5G, due to the need for numerous models of individual base stations and user equipment, which becomes cost-prohibitive.
Innovation Solution
Implementing cell clusters and input clusters using a clustering engine that generates models for groups of cells and inputs, reducing the number of computations required by applying algorithms like spectral clustering, K-means, and hierarchical clustering to determine optimal cluster numbers and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are created for each individual base station and user equipment to accurately predict network demand and performance, then prediction accuracy is improved, but computational resources and time requirements increase significantly
Solution Approach 1:
The patent combines multiple individual cell models into a single cell cluster model that represents a group of cells with similar characteristics. This merging reduces the total number of models from N individual cell models to M cluster models where M < N, thereby reducing computational resources while maintaining prediction accuracy through representative clustering
2Manufacturing precision
If models are created for each individual base station and user equipment to accurately model network behavior, then modeling precision is improved, but the time required to create and update models increases
Solution Approach 1:
Multiple individual cell models are merged into a single cell cluster model, reducing model creation time from O(N) individual model creations to O(M) cluster model creations where M < N. The clustering process groups cells with similar characteristics, allowing one model to represent multiple cells efficiently
3Adaptability or versatility
If models are frequently updated to reflect modern networking approaches and flexibility, then adaptability is improved, but computational resources and time consumption increase
Solution Approach 1:
The cell cluster model consolidates multiple individual cell models into a single updated model when network changes occur. Instead of updating N individual models, the system updates one cluster model that represents M cells, reducing computational overhead while maintaining adaptability to network changes through the clustered representation
4Reliability
If a large number of individual models are created to model large-scale complex networks, then modeling completeness is improved, but computational cost becomes prohibitive
Solution Approach 1:
The patent merges N individual cell models into M cell cluster models where M < N, reducing computational cost from O(N) to O(M). The clustering is performed based on similarity metrics that ensure representative sampling, maintaining modeling completeness for large-scale networks while reducing computational burden to feasible levels
5Adaptability or versatility
If models are updated frequently to reflect network changes and modern networking approaches, then model relevance is improved, but time and resources for modifications increase
Solution Approach 1:
The cell cluster model allows frequent updates by consolidating changes from multiple individual cells into a single model update operation. When network conditions change, the cluster model can be retrained or adjusted once to reflect changes across all represented cells, reducing modification time from O(N) individual updates to O(M) cluster updates where M < N
Data Source
AI summary
Concepts and technologies are disclosed for creating and using cell clusters. Cellular network data associated with a cellular network can be obtained. The cellular network data can include configuration data associated with a cell of the cellular network and a performance indicator associated with the cellular network. A number of cell clusters to be generated can be determined and the cell clusters can be generated. The cell clusters can include a cell cluster that can represent multiple cells including the cell. A model that represents the cell cluster can be trained. An input cluster that represents multiple inputs can be generated. The inputs can be associated with the multiple cells and the input cluster can include a value. The value can be provided as input to the model to obtain a predicted output associated with the cell cluster.