Automated Voice Testing Platform for LTE Network Diagnostics
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Solution Overview
Problem
Current network diagnostic tools fail to identify issues with individual network devices in LTE networks, relying on user complaints and unable to monitor audio quality, leading to inefficient troubleshooting and resource wastage.
Innovation Solution
An automated network voice testing platform that initiates voice communication sessions with media resource function processors to identify device malfunctions, generate reports, and perform corrective actions, using AI and machine learning to analyze audio data characteristics and reduce network load during peak hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated voice testing is implemented to identify network device issues, then detection accuracy and timeliness improve, but system complexity and resource consumption increase
Solution Approach 1:
The patent introduces an automated voice testing platform as an intermediary system between network devices and diagnostic processes. This platform includes testing components that generate test calls, analyze audio characteristics, and identify network device issues without requiring direct complex interactions between diagnostic tools and individual network devices. The platform mediates the testing process by managing multiple test sessions, coordinating with media resource function processors, and centralizing analysis functions.
Solution Approach 2:
The testing platform enables network devices to be self-diagnosed through automated testing. The system automatically generates test voice communications, analyzes the audio data for anomalies, and identifies issues without requiring manual intervention or complex external diagnostic tools. The platform performs self-service by managing its own testing workflows, generating reports, and triggering corrective actions autonomously.
2Reliability
If extensive audio data analysis is performed to identify network issues, then diagnostic capability improves, but processing time and computational resources increase
Solution Approach 1:
The testing platform performs preliminary actions by proactively initiating test voice communications before actual network issues affect users. The system schedules and executes test calls during off-peak hours or in advance, analyzing audio characteristics beforehand to identify potential problems. This preliminary testing allows the system to detect issues before they impact service quality, reducing the time needed for reactive troubleshooting.
Solution Approach 2:
The patent replaces manual diagnostic processes with automated electronic analysis systems. Instead of manual audio analysis and troubleshooting, the system uses automated algorithms to process voice communications, extract features, and identify network device issues. This substitution of mechanical/manual processes with automated electronic systems significantly reduces processing time while maintaining or improving diagnostic accuracy.
3Reliability
If multiple voice communication sessions are initiated for testing, then network device coverage improves, but network load and resource consumption increase
Solution Approach 1:
The testing platform applies partial action by initiating a controlled number of test voice communications rather than exhaustive testing of all possible network paths simultaneously. The system strategically selects representative test sessions that provide sufficient coverage of network devices without overwhelming the network. By performing excessive testing only when necessary and using partial testing during normal operations, the system balances coverage requirements with network load constraints.
Solution Approach 2:
The system implements periodic testing by scheduling voice communication tests at intervals rather than continuously. Test sessions are conducted periodically during off-peak hours or at scheduled times to reduce network load. This periodic approach maintains network device coverage while avoiding constant testing that would consume excessive network resources and energy.
Data Source
AI summary
A device initiates voice communication sessions with a media resource function processor (MRFP) of a network, where each voice communication session communicates audio data via one or more network devices of the network. For each voice communication session, the device sends first audio data to the MRFP and receives second audio data from the MRFP via the voice communication session, processes the first audio data and second audio data to determine one or more characteristics of the second audio data, and generates a record concerning the voice communication session based on the one or more characteristics of the second audio data. The device generates a report based on a respective record of each voice communication session, processes the report using an artificial intelligence technique to identify an issue concerning at least one network device, and performs, based on identifying the issue, an action concerning the at least one network device.