Synthetic Data Injection for 5G Network Failure Thresholds
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Solution Overview
Problem
The increasing complexity of mobile networks with numerous components creates potential failure points that are difficult to identify and predict, especially due to the dynamic and unpredictable data mixes encountered in real-world scenarios.
Innovation Solution
A method and system that utilize a computing device to inject synthetic data of varying types and amounts into a 5G network, employing a machine learning model to determine failure points and generate dynamic thresholds for network component management, using a cloud-based architecture to simulate real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of network components is increased to handle more data types and traffic, then the network capacity and versatility are improved, but the number of potential failure points increases and system reliability deteriorates
Solution Approach 1:
The system performs preliminary failure analysis by injecting synthetic data and progressively increasing data amounts to identify failure points before they occur in production. This proactive approach allows the network to be configured with appropriate redundancy and failover mechanisms in advance, resolving the contradiction between increased capacity and maintained reliability.
Solution Approach 2:
The system creates a synthetic copy of the production network environment with identical component topology and data flow patterns. By testing failure scenarios in this synthetic copy rather than the live network, the system can identify vulnerabilities without compromising actual network reliability, allowing safe exploration of failure modes.
2Reliability
If synthetic data injection testing is performed to identify failure points, then network reliability is improved, but the time and resources required for testing increase
Solution Approach 1:
The testing process is segmented into discrete phases: initial synthetic data injection at baseline levels, progressive increase of data amounts in controlled increments, and staged identification of failure points. This segmentation allows systematic exploration of failure modes without requiring exhaustive testing, reducing overall testing time while maintaining reliability assessment accuracy.
Solution Approach 2:
The system applies partial action by injecting synthetic data only to the extent necessary to identify failure points, rather than exhaustively testing all possible data volumes and combinations. Once failure thresholds are identified, further testing is curtailed, optimizing the balance between reliability assessment and time investment.
3Reliability
If dynamic thresholds are implemented for proactive failure prevention, then network robustness is improved, but the complexity of monitoring and control systems increases
Solution Approach 1:
The system implements feedback loops where synthetic data injection results feed into dynamic threshold calculations, which in turn guide subsequent testing and production monitoring. This automated feedback mechanism enables the system to adaptively adjust thresholds based on observed failure patterns without requiring complex manual configuration, balancing robustness with manageable system complexity.
Solution Approach 2:
The system performs self-service by automatically generating synthetic data, injecting it into the network, monitoring for failures, and deriving dynamic thresholds without external intervention. This automation reduces the operational complexity of maintaining sophisticated monitoring systems while achieving high network robustness through continuous self-optimization.
Data Source
AI summary
A method may include providing an amount of first data and increasing the amount of first data until a first network component fails. The method may include determining first failure data associated with the failure of the first network component. The method may include providing an amount of second data and increasing the amount of second data until a second network component fail. The method may include determining second failure data associated with the failure of the second network component. The method may include generating synthetic data. The method may include providing the synthetic data such that one or more network components fail. The method may include determining third failure data associated with the failure of the network component and the synthetic data. The method may include generating the network parameters based at least in part on the third failure data.