Data intelligence model for operator data queries

Abstract

Operators managing a cloud RAN collect vast amounts of data, e.g., node-level data, gNodeB level data, user level data, and flow-level data, which are utilized for network monitoring, evaluating key performance indicators (KPIs), and nodes management. Retrieving and visualizing information and values of different metrics is critical to managing network operation; however, data retrieval on large datasets is challenging. While foundation models perform poorly on large datasets, an accurate answer to a data query is generated by providing semantically similar metrics as context to a foundation model, thereby limiting the number of counters needed for processing the data query. The foundation model then generates a first output of metrics relevant to answering the data query and, based on the first output, generates a second output comprising query code (e.g., SQL or KQL) for computing the answer, thereby improving mathematical accuracy of the answer.