Cell Coverage Morphology Similarity for Network Optimization
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Solution Overview
Problem
Conventional methods for determining cell similarity in mobile communications networks are limited in functionality and usefulness, failing to quantify the similarity between cells based on their morphology, which hinders effective network analysis and optimization.
Innovation Solution
A method and apparatus that quantify the similarity between cells by analyzing their coverage area morphology using features such as clutter types, terrain elevation, and antenna configurations, employing a cosine similarity approach to calculate a similarity coefficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional classification or clustering algorithms are used to group cells with similar characteristics, then cells can be organized into clusters based on performance indicators or cell issues, but the similarity between cells cannot be quantified and the applicability is limited to specific use cases
Solution Approach 1:
The patent transforms the cell similarity assessment from qualitative clustering to quantitative measurement by introducing a similarity coefficient calculated through parameter comparison. The system extracts multiple parameters including cell configuration parameters (antenna azimuth, tilt, height, bandwidth, power) and coverage area morphology parameters (area, perimeter, shape factors, terrain features), then computes a numerical similarity coefficient that enables precise quantification and broad applicability across different network optimization scenarios
2Reliability
If cell similarity is determined using specific cell features for clustering, then cells with similar performance or issues can be grouped, but the results cannot be applied to cells affected by different characteristics
Solution Approach 1:
The patent creates a universal cell similarity assessment framework that can evaluate any pair of cells regardless of their specific characteristics. By using a comprehensive set of parameters covering cell configuration, coverage morphology, and network performance, the system can reliably group cells by any desired criterion and apply the same methodology across diverse cell types, frequency bands, and network scenarios
3Productivity
If conventional approaches focus on cell performance indicators and issues, then cells can be clustered for specific optimization tasks, but the functionality is limited and not useful for morphology-based analysis
Solution Approach 1:
The patent implements a dynamic and flexible cell similarity assessment system where the relevance of different parameters can be adjusted based on the specific optimization task. The system calculates a comprehensive similarity coefficient that can be weighted or filtered to emphasize different parameter categories depending on whether the goal is coverage optimization, capacity planning, or interference management, thereby achieving both high productivity and broad versatility
Data Source
AI summary
A method (50) and apparatus (60) for quantifying similarities between first and second cells (12a, 12b) in a mobile communications network (10) is provided. In particular, for each of the first cell and the second cell, a network node (60) receives input parameters, and based on those parameters, determines (54) respective sets of features for the cell. Each respective set of features defines a cell configuration and a coverage area morphology for the cell. So determined, a level of similarity between the first and second cells can be determined (56) based on their respective sets of features.