RAN Test Case Generation Using Production KPI Matching
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Solution Overview
Problem
Network operators face challenges in efficiently reproducing production network traffic in a lab to maintain quality control of wireless telecommunication networks due to evolving UE hardware and software, as well as new RAN functionalities and applications.
Innovation Solution
A system that generates a test case for a test RAN based on key performance indicators (KPIs) from a production RAN, mimicking production conditions to detect network issues early and improve quality control by adjusting traffic types, characteristics, and UE capabilities, and iteratively refining simulations until KPIs match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If production network traffic is reproduced in a lab environment, then quality control of the wireless telecommunication network is maintained, but the complexity of reproducing evolving UE hardware, software, and RAN functionalities increases
Solution Approach 1:
The patent creates virtual copies of production network traffic patterns using synthesized test cases that replicate key performance indicators and traffic characteristics without requiring physical reproduction of the entire production environment. This allows quality control testing while avoiding the complexity of exact environmental replication.
Solution Approach 2:
The system transforms production KPIs into relevant test parameters and adjusts traffic characteristics, UE capabilities, and simulation conditions to match production behavior. By changing and optimizing parameters iteratively, the system maintains reliability without requiring complex environmental reproduction.
2Reliability
If test cases are created to match production KPIs, then network issues can be detected early, but the time and resources required to iteratively refine simulations increase
Solution Approach 1:
The system performs preliminary transformation of production KPIs into test parameters and creates initial test cases before actual testing begins. This preliminary preparation reduces the iterative refinement time by establishing a solid foundation that closely approximates production conditions from the start.
Solution Approach 2:
The system compares simulation KPIs with production KPIs and uses the differences to iteratively adjust test cases. This feedback loop enables early issue detection while systematically reducing refinement time by focusing adjustments on the most significant deviations rather than exhaustive re-simulation.
3Measurement precision
If simulation parameters are adjusted to match production traffic characteristics, then accuracy of quality control testing is improved, but the complexity of managing multiple parameters increases
Solution Approach 1:
The system extracts only the most relevant KPIs and traffic characteristics from the complex production environment that are necessary for quality control testing. By selecting and focusing on critical parameters rather than reproducing all production details, the system achieves high testing accuracy while reducing parameter management complexity.
Data Source
AI summary
The system obtains KPIs of the production RAN of the network and capabilities of multiple UEs interacting with the test RAN. The system creates a test case based on the KPIs and the capabilities of the multiple UEs interacting with the test RAN by: adjusting a type of traffic associated with the test RAN, adjusting traffic characteristics, and adjusting the capabilities of the multiple UEs interacting with the test RAN. The system runs a simulation of the test case interacting with the test RAN to obtain simulation KPIs. The system determines whether the simulation KPIs correspond to the KPIs associated with the production RAN. Upon determining that the simulation KPIs correspond to the KPIs of the production RAN, the system stores the test case in a database. Upon determining that the simulation KPIs do not correspond to the KPIs of the production RAN, the system adjusts the test case.