Method and network device for determining causes of network slice performance degradation in an open radio access network, O-RAN

A network device for determining causes of network slice performance degradation in an open radio access network (O-RAN), the O-RAN being under Central Unit-Distributed Unit (CU-DU) functional split, the network device comprising a functional unit having one or more computational processors with access to memory, which, alone or in combination, are configured to provide for execution of the following steps:

• in a setup phase:
• obtaining telemetries that contain multivariate observations of DUs,
• aggregating, per CU per network slice, telemetry data obtained from involved DUs,
• extracting, for a specific time slot, high order features from correlations between the multivariate observations across the involved DUs, and
• based on the telemetries and the extracted high order features, learning a k-time slots ahead performance prediction model;
• and
• in a detection phase:
• using, upon receiving new telemetries, the performance prediction model to forecast the network slice performance,
• based on a performance degradation being forecasted, sampling a set of performance prediction models and computing impact scores based on gradients of all telemetries and the extracted high order features using the sampled performance prediction models, and
• selecting telemetries and high order features with impact scores exceeding a configurable threshold and identifying network slice entities related to the selected telemetries as root causes of the forecasted performance degradation.

A non-transitory, computer-readable storage medium having instructions thereon which, upon execution on one or more processors, provide for execution of the following steps:

• in a setup phase:
• obtaining telemetries that contain multivariate observations of DUs of an open radio access network (O-RAN), the O-RAN being under Central Unit-Distributed Unit (CU-DU) functional split,
• aggregating, per CU per network slice, telemetry data obtained from involved DUS,
• extracting, for a specific time slot, high order features from correlations between the multivariate observations across the involved DUs, and
• based on the telemetries and the extracted high order features, learning a k-time slots ahead performance prediction model;
• and
• in a detection phase:
• using, upon receiving new telemetries, the performance prediction model to forecast the network slice performance,
• based on a performance degradation being forecasted, sampling a set of performance prediction models and computing impact scores based on gradients of all telemetries and the extracted high order features using the sampled performance prediction models, and
• selecting telemetries and high order features with impact scores exceeding a configurable threshold and identifying network slice entities related to the selected telemetries as root causes of the forecasted performance degradation.