ON-DEVICE HYBRID MACHINE LEARNING MODEL FOR CALL OPTIMIZATION

Abstract

Embodiments of the present disclosure disclose method and apparatus optimizing call quality in a user equipment (UE). The method includes: identifying a mobile originated (MO) call or a mobile terminated (MT) call satisfying one or more criteria; capturing a plurality of parameters associated with the MO call or the MT call and the UE, based on the MO call or the MT call satisfying the one or more criteria and correlating the plurality of parameters with historical call data to identify one or more patterns influencing the call quality; analyzing, using a hybrid machine learning (ML) model, the one or more identified patterns and predicting call quality issues for the MO call or the MT call; and adjusting UE resources based on the predicted call quality issues and real time context data and adjusting includes providing recommendations for a user of the UE.

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