Cellular Call Failure Classification for Root Cause Resolution
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Solution Overview
Problem
Existing cellular networks face challenges in quickly and accurately identifying the root causes of call failures, leading to inefficient problem resolution and resource wastage in troubleshooting.
Innovation Solution
A performance server classifies call failure samples into distinct sets based on coverage and signal quality parameters, identifies root causes, and recommends remedial actions, while also determining the impact of new cell site deployments on improving network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual troubleshooting methods are used to identify root causes of call failures, then network operators can resolve issues, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables automatic self-diagnosis of call failure root causes by having the network infrastructure itself collect, analyze, and identify problems without human intervention. The performance server automatically processes call failure data, classifies samples, and determines root causes, allowing the network to serve its own troubleshooting needs efficiently
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with an automated electronic system. Instead of network operators manually analyzing call failure data through traditional methods, an electronic performance server automatically collects, processes, and analyzes call failure samples using computer algorithms to identify root causes, substituting human effort with automated computational processes
2Reliability
If extensive manual investigation and multiple remedial measures are applied to resolve call failures, then problems can be fixed, but computing resources and network bandwidth are wasted
Solution Approach 1:
The system performs preliminary classification of call failure samples into distinct sets based on coverage and signal quality parameters before root cause analysis. By pre-organizing data into structured categories, the system prepares the information in advance for more efficient and accurate root cause identification, reducing the need for repeated investigative actions
Solution Approach 2:
The patent segments call failure data into distinct sample sets based on coverage parameters and signal quality parameters. This segmentation divides the complex troubleshooting problem into manageable categories, allowing the system to focus analysis on specific conditions and identify root causes more efficiently without examining every possible factor in detail
3Productivity
If new cell sites are deployed without impact analysis, then network coverage is improved, but resources may be wasted on low-impact deployments
Solution Approach 1:
The system performs preliminary impact analysis of proposed new cell sites before actual deployment. By evaluating the potential impact of each proposed cell site on resolving identified call failure issues, the network operator can prioritize deployments that will provide the greatest performance improvement, avoiding waste of resources on low-impact locations
Data Source
AI summary
Data relating to call failures that occurred in a wireless communication network is accessed from a memory, the data including a plurality of call failure samples. The call failure samples are classified into a plurality of sets, wherein each set is associated with a first range of values of the coverage parameter and a second range of values of the signal quality parameter. One or more remedial actions associated with a particular set of the call failure samples is obtained, wherein each set of the call failure samples is associated with respective at least one remedial actions. An indication is generated that the one or more remedial actions is to be performed to resolve call failure events associated with call failure samples in the particular set of the call failure samples.