This topic describes how the Uhana by VMware platform uses artificial intelligence (AI) to detect poor subscriber experiences and provide root causes to help fix problems in the radio access network (RAN).
The following topics describe how alerts are treated in the Uhana by VMware platform.
The Uhana by VMware platform ingests raw signaling and traffic traces and processes them into subscriber session records and cell records. These processed records serve as inputs for building AI-based applications on top of the real-time stream processing engine (RSPE).
For more information, see Understand data sources and events.
The following topics list some critical key performance indicators (KPIs) in these records.
Cell records contain KPIs that are periodically reported by cells.
The Uhana by VMware platform uses offline subscriber session and cell records to train models about subscriber experience KPIs.
The following are several examples of subscriber experience metrics which illustrate how Uhana by VMware models might be trained.
With these models, Uhana by VMware can access the impact of a RAN feature on a subscriber's experience.
Using the models trained offline, the Uhana by VMware platform infers in real-time, the root-cause of degraded quality subscriber experiences in the RAN and provides the impact of resolving the problem.
For every subscriber session whose experience does not satisfy operator-defined expectations, the Uhana by VMware platform identifies anomalies in RAN features that can degrade subscriber experience. These anomalies are identified by learning these features' nominal values for the network and inferring deviations from these values. Each anomaly is then associated with a root-cause label. The impact on subscriber experience is deduced by adjusting the anomalous features to their appropriate nominal values and applying the offline model on the new feature set to derive the new subscriber experience.
The following RAN root cause descriptions are supported.
The Uhana by VMware platform performs a spatiotemporal clustering of sessions that are impacted by the same problem and forms alerts. The spatiotemporal clustering gets a complete view of the impact caused by a specific RAN problem and also reduces the number of duplicate alerts generated.
The following properties are included in each alert description.
Each alert has a Visibility page that provides KPIs relevant to the alert and an Insights page that describes potential fix recommendations for the alert.