Cell Malfunction Identification via Spatial Call Drop Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for optimizing telephone cell network coverage are time-consuming and complex, requiring iterative modifications of cell parameters to address call drops, which hampers efficient identification and classification of malfunctions.
Innovation Solution
A method that associates spatial information with call drops to classify malfunctions, allowing targeted modifications of telephone cell parameters by analyzing data on call endings and signal connections, including the use of call detail records and packet switch events to identify and quantify issues like refraction effects and user mobility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative modification of cell parameters is used to optimize network coverage, then network coverage is improved, but time consumption and process complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-classifying malfunctions into distinct types (boundary malfunction, coverage hole, excessive coverage) based on spatial analysis of call drop patterns before optimization begins. This classification is performed by analyzing the geographic distribution of call drops relative to cell boundaries and neighboring cells, allowing the system to identify the specific malfunction type in advance and select the appropriate optimization strategy immediately, eliminating the need for iterative trial-and-error parameter modifications.
2Reliability
If iterative modification of cell parameters is used to optimize network coverage, then network coverage is improved, but process complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the optimization process into distinct stages: (1) spatial analysis of call drop locations, (2) classification into malfunction types based on geographic patterns, and (3) application of specific optimization strategies for each type. This segmentation transforms a complex iterative process into a structured, step-by-step procedure where each stage has a clear objective and methodology, significantly reducing overall process complexity.
Solution Approach 2:
The patent applies local quality by tailoring the optimization strategy to the specific type of malfunction identified in each cell. Instead of applying uniform iterative modifications across all cells, the system selects different optimization approaches based on the local characteristics of the malfunction: boundary adjustment for boundary malfunctions, power reduction for coverage holes, and power reduction with neighbor adjustments for excessive coverage. This localized approach simplifies the overall process by avoiding unnecessary trial-and-error iterations.
3Productivity
If spatial information is associated with call drops to classify malfunctions, then targeted optimization is enabled, but data processing requirements increase
Solution Approach 1:
The patent applies the taking out principle by extracting and isolating only the critical spatial information needed for malfunction classification: the geographic coordinates of call drops, cell boundary locations, and distances to neighboring cells. Rather than processing all available network data, the system focuses exclusively on these essential spatial parameters, significantly reducing data processing volume while enabling effective malfunction classification and targeted optimization.
Data Source
Figure 1
Figure 2
AI summary
Method for identifying malfunctions in the management of calls in a telephone cell comprising the steps of: providing (3) a plurality of telephone cells (53) in signal communication with a data processing unit (52) and with a memory unit (50); detecting (4) call endings that took place in the telephone cell (53) and signal connections between a user and the telephone cell (53); acquiring a first set of data (5) relating to each call ending detected by each telephone cell (53), the first set of data comprising at least one identifier code of the user of the call ending and a time instant of the call ending and geographic coordinates of the cell (53) in which the call ending is detected; acquiring a second set of data (6) relating to each signal connection detected by each telephone cell (53); identifying (8) which ones of the call endings detected are dropped calls; associating (9) with each identified dropped call the second set of data for each signal connection between the identifier code of the user associated with the identified dropped call and the telephone cell (53) that took place in a first time interval comprised between a first and second time instant respectively before and after the time instant of the dropped call for each identified dropped call in each telephone cell (53) to build at least one index relative to geographic coordinates of the telephone cell associated with the dropped call and of at least another telephone cell (53); calculating (11) an effective mean of the built indices; processing (13) the effective mean and a sample mean provided to calculate a significant parameter for each telephone cell; comparing (14) the significant parameter with a pre-set threshold value; signalling a malfunction (15) of the telephone cell (35) when the value of the significant parameter is greater than the pre-set threshold value.