AI-Based Cell Handover Configuration for Predictive Target Selection
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Solution Overview
Problem
Wireless communication systems face issues with poor accuracy and high failure rates in cell handover processes due to suboptimal handover configurations.
Innovation Solution
A handover configuration method utilizing AI/ML models to predict cell handover events based on probability, measurement quantities, and future time periods, along with threshold and offset values, to enhance the accuracy and success rate of cell handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional handover configuration methods are used, then the handover process is simple, but the accuracy of handover to target cell is poor and failure rate is high
Solution Approach 1:
The patent applies preliminary action by configuring handover parameters in advance before handover events occur. The network device pre-configures probability thresholds, measurement quantities, and AI/ML model parameters so that when handover conditions are met, the terminal can execute handover decisions quickly and accurately without complex real-time calculations, thus improving handover accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent introduces AI/ML models as intermediaries between traditional measurement methods and handover decisions. These models process measurement quantities and probability information to generate optimized handover recommendations, acting as a mediator that enhances decision accuracy without requiring the terminal to implement complex handover logic directly, thereby improving accuracy while managing complexity.
2Reliability
If traditional measurement-based handover methods are used, then the handover process is straightforward, but the success rate of cell handover is low
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple handover parameters including probability thresholds, measurement quantities, and AI/ML model settings before handover events occur. This allows the system to make more reliable handover decisions based on pre-analyzed conditions, improving success rate without requiring complex real-time processing at the terminal.
Solution Approach 2:
The patent implements feedback mechanisms where the terminal reports measurement quantities and handover execution results back to the network device. The network device uses this feedback to adjust AI/ML model parameters and handover configurations, creating a closed-loop system that continuously improves handover success rate while maintaining manageable complexity through automated adaptation.
3Measurement precision
If predictive configurations are implemented, then handover accuracy improves, but power consumption increases due to additional measurements
Solution Approach 1:
The patent applies partial action by implementing predictive configurations selectively rather than continuously. The network device configures prediction parameters and AI/ML models to be activated only when specific conditions are met or when beneficial, avoiding unnecessary measurements and computations. This reduces power consumption while maintaining improved accuracy where it provides the most value.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting measurement frequencies, prediction intervals, and AI/ML model complexity based on network conditions and terminal capabilities. When power consumption becomes a concern, the system can reduce measurement frequencies or simplify model operations, thereby maintaining improved accuracy when needed while reducing power consumption during normal operation.
Data Source
AI summary
This application provides a handover configuration method, a terminal device, and a network device. The method includes: A first terminal device receives, in a first cell, downlink signaling from a first network device, and determines configuration information based on the downlink signaling, where the configuration information is used to configure an event related to a probability, where the probability includes a probability of cell handover or a probability that a second cell serves as a target cell, and the second cell includes at least one of the following: the first cell, at least one intra-radio access technology neighboring cell of the first cell, or at least one inter-radio access technology neighboring cell of the first cell; the configuration information indicates to determine first information based on a first submodel; and/or the configuration information is used to configure an event related to a future measurement quantity.