Pushing machine-learning technology and big data applications is enabling Australia's National Broadband Network (NBN) to be more "proactive" in analysing and repairing network health, the company has said.
Speaking during the annual CeBIT conference in Sydney on Tuesday morning, NBN executive general manager of IT Strategy and Architecture Arun Kohli said NBN is collecting real-time data from "every part of the network which is connected or can be connected".
"On top of the typical applications of insights, metrics, and predictive analytics, we've gone to the next level of machine learning to be more proactive for the customer experience," Kohli said.
"Also, this helps us optimise our program -- what kind of technology we have to do, what kind of issues can come up in future when we decide the upgrade."
Kohli said network data already collected by NBN includes modulation stats, sync and error rates, frame loss and delay, and alarms and faults. It is next looking to collect data on micro-reflections, spectral response curves, and throughput over time.
NBN has also successfully trained machine learning models to recognise various impairment types from spectral data, he said. As a result, the company is able to discover in-house issues such as bridged taps -- unterminated branches of copper pairs in the premises that cause signal reflections and result in speed loss -- and outside plant issues such as copper pair corrosion caused by water penetration, which results in speed loss and service instability.
"When we look into the in-house copper data of the connectivity data [combined] with the machine learning ... we can predict two things: That services may start degrading over time -- the service is still there and probably the end user doesn't know, but over time it