Efficient initial acquisition with gain state prediction using machine learning

Abstract

Methods, systems, and devices for wireless communications are described. For example, the described techniques provide for a user equipment (UE) to predict a gain state for an initial acquisition procedure. For example, the UE may use a machine learning (ML) model to predict an initial gain state which is likely to result in a successful initial acquisition procedure (e.g., without adjusting the gain state and reattempting the initial acquisition procedure). The UE may input UE history data or crowdsourced data (e.g., from a local network or a cloud-based server) into the ML model to generate a predicted gain state which may be more likely to result in a successful initial acquisition (e.g., without reattempting the initial acquisition procedure). The UE may use the initial gain state generated by the ML model to attempt an initial acquisition procedure with a network entity.