AI Cell Handover Preparation for Lower Network Switching Latency
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Solution Overview
Problem
Existing communication systems face inefficiencies in cell handover processes, particularly in predicting and preparing for seamless transitions between cells to maintain mobile communication services.
Innovation Solution
Implementing artificial intelligence (AI) to predict an AI target cell and perform pre-emptive interaction with the target base station, utilizing various information such as measurement, load, and geographical data to optimize handover preparation and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional cell handover methods are used, then handover process is simple, but handover latency is high and efficiency is low
Solution Approach 1:
The patent applies preliminary action by predicting the AI target cell in advance before the actual handover occurs. The source base station uses AI algorithms to predict which cell the terminal device will hand over to, and performs resource allocation and interaction with the target base station beforehand. This pre-preparation significantly reduces the handover latency and improves handover efficiency, as the target cell is already ready to receive the terminal device when handover is actually needed.
2Productivity
If AI prediction is used to determine target cell, then handover efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an AI prediction module as an intermediary between the traditional handover decision-making process and the actual handover execution. This AI module analyzes various parameters (signal quality, load information, geographical data) and outputs a predicted target cell, which then guides the handover process. By inserting this intelligent intermediary layer, the system achieves improved handover efficiency while managing complexity through modular AI integration rather than fundamentally redesigning the entire handover architecture.
Solution Approach 2:
The patent replaces the traditional mechanical/manual cell handover decision-making process with an AI-based predictive system. Instead of relying solely on real-time signal quality thresholds and manual base station decisions, the system uses AI algorithms to automatically predict the optimal target cell based on historical data, current conditions, and movement patterns. This substitution of mechanical decision-making with intelligent automation improves efficiency while the AI module handles the complexity of analyzing multiple parameters simultaneously.
3Productivity
If pre-emptive interaction with target base station is performed, then resource allocation is optimized, but signaling overhead increases
Solution Approach 1:
The patent applies preliminary action by having the source base station send a first request message to the target base station in advance of the actual handover. This request message includes AI prediction information and resource allocation requests. The target base station can prepare resources beforehand and confirm availability, optimizing resource utilization. The signaling overhead is managed by consolidating multiple pieces of information (AI prediction data, resource requirements, handover parameters) into a single structured request message, reducing the total number of separate signaling exchanges needed.
Data Source
AI summary
A cell handover method and an apparatus are provided. The method includes: determining an AI target cell, where the AI target cell is a predicted serving cell to which a terminal device can be handed over; and sending a first request message, where the first request message is used to request a network device corresponding to the AI target cell to allocate, to the terminal device, a resource corresponding to the AI target cell. The first request message indicates at least one of the following: identification information of the AI target cell, a type of the handover being AI handover, activation time information, expiration time information, prediction accuracy of the AI target cell, or the like. The activation time information indicates an earliest moment at which the terminal device is handed over to the AI target cell.