Infovista has announced Precision Drive Testing to bring an ML/AI data-driven approach and automation to network testing, significantly reducing the cost and time of 5G network testing. The patent-pending Precision Drive Testing leverages 5G network, service and customer data, and ML/AI techniques to increase the speed and accuracy of the 5G testing process.
“There’s a misconception that 5G makes drive testing redundant, but the reality is that although enhanced from LTE, the ‘minimisation of drive testing’ feature in 5G still only works if users are in the geographic area that needs testing. Drive testing will still be needed to complement the gaps of MDT in testing, at least until 5G is fully autonomous,” said Dr Irina Cotanis, Technology Director, Network Testing at Infovista. “But such is the complexity of 5G networks and the proliferation of device types, that traditional drive testing processes are not fit-for-purpose. Operators can’t afford for their highly qualified RF engineers to be driving around manually testing; it’s time to automate and make the cloud do the heavy lifting. Precision Drive Testing transforms the drive testing process from being engineering-driven to AI/ML data-driven, from manual to autonomous and from something very few can do, to something that can be done by anyone.”
Infovista’s cloud-based Precision Drive Testing solution automates and guides testing triggered by use cases based on information and/or analytics results from network planning and performance, fault and configuration management, services assurance, and customers’ (or Crowdsource) data, which an operator will use to inform and improve the network lifecycle processes. Results are instantly available as actionable insights to be used by the requesting systems/solutions, creating a closed-loop of automated and guided testing. This reduces testing time and effort and improves the accuracy of service-specific testing KPIs.
When triggered, the Precision Drive Testing use cases automatically calculate the best test route, and generate test scripts to run along with their Definition of Done criteria, while the correspondent KPIs log masks and context-sensitive criteria. The automatic calculation of sweet spots and drive routes, coupled with directions to the tester, including error handling and Edge Analytics, improve the speed and accuracy of the testing processes. Finally, real-time reporting and data feed of the expected result, ensures that testing data is quickly acted upon and, in turn, used to inform the network/service/customer data source on which the drive has been triggered.