ML Gain State Prediction for Wireless Initial Acquisition
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently acquiring initial connections due to inadequate gain state settings, leading to delays, increased latency, and reduced user experience.
Innovation Solution
The use of machine learning (ML) models to predict an initial gain state for user equipment (UE) in wireless communication systems, based on historical data and crowdsourced information, to optimize the initial acquisition procedure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a default gain state is used for initial acquisition, then the procedure is simple to implement, but the acquisition time increases and success rate decreases
Solution Approach 1:
The system performs preliminary actions by collecting historical acquisition data and training an ML model before the actual initial acquisition procedure. The model predicts the optimal gain state in advance, allowing the UE to directly apply the predicted value without iterative adjustments, thereby reducing acquisition time and improving success rate.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting data from previous acquisition procedures and using this feedback to train and improve the ML model. The model learns from historical outcomes (successful/failed acquisitions) to refine its predictions of optimal gain states, progressively improving acquisition reliability.
2Reliability
If iterative gain state adjustments are performed to achieve successful acquisition, then the success rate improves, but the latency and processing time increase
Solution Approach 1:
The patent replaces the mechanical iterative adjustment process with an intelligent prediction system. Instead of physically attempting acquisitions with different gain states in sequence, the ML model substitutes this mechanical trial-and-error process by predicting the optimal gain state directly, dramatically reducing the duration of the acquisition procedure.
Solution Approach 2:
The system changes the parameter selection approach from static default values to dynamic predicted values. The ML model analyzes historical data to determine optimal gain state parameters for specific conditions, allowing the system to adapt parameters based on learned patterns rather than using fixed iterative adjustments.
3Productivity
If machine learning models are implemented for gain state prediction, then acquisition efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the complexity by separating the ML model training phase from the inference phase. The computationally intensive training occurs in the background using collected historical data, while the actual acquisition process only requires running the trained model for predictions, significantly reducing the processing complexity during critical acquisition moments.
Solution Approach 2:
The system performs preliminary model training and preparation before actual acquisition procedures. By pre-processing the data and training the model in advance, the complex computational work is completed beforehand, leaving only lightweight prediction operations for the time-critical acquisition process.
Data Source
AI summary
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.