Network Handover Optimization via Predicted Mobility Maps
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Solution Overview
Problem
Current network infrastructure faces congestion issues due to high control plane traffic, particularly during frequent handovers of mobile devices, which can be optimized by predicting device locations and adjusting communication parameters, but existing methods do not effectively minimize control plane traffic in high mobility scenarios.
Innovation Solution
A method and terminal for managing handovers by predicting a terminal's journey using sensor and time data, generating a mobility map, and transmitting it to a navigation server to determine high mobility mode, thereby reducing control plane traffic by assigning the terminal to a macrocell upon the first handover request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional handover management is used in high mobility scenarios, then handover responsiveness is improved, but control plane traffic increases and network congestion worsens
Solution Approach 1:
The system performs preliminary actions by predicting the terminal's future journey and identifying high mobility modes before actual handovers occur. The mobility map is generated in advance using sensor data and time data, allowing the network to prepare optimal handover parameters and assign terminals to macrocells proactively, thereby reducing control plane traffic during actual handover events.
Solution Approach 2:
The system changes handover parameters dynamically based on predicted mobility patterns. When high mobility mode is detected, the network adjusts handover thresholds, timing, and target cell selection parameters to reduce the frequency and complexity of handovers, thereby decreasing control plane traffic while maintaining handover responsiveness.
2Reliability
If frequent handovers are executed to maintain connection quality, then service continuity is improved, but network resource utilization deteriorates
Solution Approach 1:
The system generates a mobility map in advance that predicts the terminal's journey and identifies optimal handover points. By preparing handover parameters beforehand and assigning high mobility terminals to macrocells, the system ensures service continuity while avoiding unnecessary handovers that would consume network resources.
Solution Approach 2:
The system uses sensor data and time data as feedback to continuously update the mobility map and adjust handover strategies. This feedback mechanism allows the network to optimize handover decisions based on actual terminal behavior patterns, maintaining service continuity while improving network resource utilization through data-driven parameter adjustments.
Data Source
Figure 1~2
AI summary
The invention is directed to systems, methods and computer program products for managing handover of a terminal on a network. An exemplary method comprises: receiving sensor data associated with the terminal; receiving time data associated with the terminal; predicting a journey of the terminal based on at least one of the sensor data and the time data; generating a mobility map based on the predicted journey; and transmitting the mobility map to the network.