Predictive Inter-Cell Mobility Using AI Beam Handover Timing
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Solution Overview
Problem
Conventional inter-cell mobility schemes are reactive and prone to unintended events such as handover failures, radio link failures, and throughput losses, especially in high-mobility scenarios or high-density micro cell environments, and do not adequately enhance robustness.
Innovation Solution
Implementing a proactive inter-cell mobility technique using artificial intelligence and neural networks to predict optimal beam sets and activation times for handovers, reducing reliance on reactive measurement-based triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive inter-cell mobility schemes are used, then handover decisions are made based on past measurement results, but handover failures and radio link failures occur in high-mobility scenarios
Solution Approach 1:
The neural network performs predictive analysis to determine future handover timing and target cells before the actual handover event occurs. By processing historical measurement data and predicting future radio conditions, the system proactively prepares handover commands in advance, allowing the terminal to execute handovers at optimally predicted times rather than reacting to past conditions, thereby reducing handover failures in high-mobility scenarios
Solution Approach 2:
The neural network acts as an intermediary between raw measurement data and handover decision-making. It processes and interprets historical measurement results to generate predictive insights about future radio conditions, which then guide handover decisions. This intermediary layer transforms reactive measurement data into proactive predictive information, improving both timing accuracy and reliability
2Productivity
If reactive measurement-based handover triggers are used, then handover decisions are made based on reported measurement events, but frequent handovers cause throughput loss in high-density micro cell environments
Solution Approach 1:
The system performs preliminary predictive analysis using the neural network to identify optimal handover timing and target cells before actual handovers are executed. By predicting future radio conditions based on historical patterns, the system可以避免不必要的频繁切换, reducing handover frequency in high-density micro cell environments while maintaining robustness through AI-based decision intelligence
Solution Approach 2:
The system utilizes historical measurement data and handover outcomes as feedback to continuously train and improve the neural network model. This feedback mechanism allows the system to learn from past handover experiences, including cases of frequent handovers and throughput loss, thereby improving future handover decisions to enhance both throughput stability and handover robustness
Data Source
AI summary
A method of a terminal may comprise: generating input data to be provided to a neural network based on at least one of quality values of a serving cell and a neighbor cell, or quality values of beams belonging to each of the serving cell and the neighbor cell; and performing an inter-cell mobility operation with a base station based on an optimal beam set predicted by the neural network using the input data, cell information corresponding to the optimal beam set, and an activation time determined according to a future time predicted by the neural network.