System and method for improving machine learning model performance in a communications network

This invention describes a way to make machine learning models work better for managing mobile phone networks. It gathers data from different parts of the network, called cellular aggregation units, and then cleans this data by filling in any missing information with the most common values. This cleaned data is then used to train the machine learning models, which can then predict how well the network will perform.

Why it matters: Since 2019, the complexity and data volume of radio access networks have dramatically increased with 5G deployment. This makes automated, ML-driven network optimization, including foundational data preparation, more critical for performance and reliability.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, ML models, network integration

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