Predictive Secondary Cell Pre-configuration for Dense Network Mobility
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Solution Overview
Problem
In wireless communication systems with dense and ultra-dense cell deployments, the short usable time of cells due to radio resource control signaling interruptions hampers mobility and throughput, especially for devices switching between secondary cells, leading to a drop in user experience.
Innovation Solution
A method where a network node predicts and pre-configures the most likely subsequent secondary cells for a user equipment (UE) based on its movement patterns, transmitting initial configuration data and event triggering conditions, allowing for quick switching between cells by preparing and activating the better cell, thereby minimizing interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radio resource control signaling is used for reconfiguring the UE before new cell activation, then connection reliability is improved, but mobility speed deteriorates due to signaling interruptions
Solution Approach 1:
The network node predicts future SCells the UE will need based on mobility patterns and spatial information, and pre-configures these cells in advance. This preliminary action eliminates the need for time-consuming RRC signaling during cell switching, as the UE can directly activate pre-configured SCells when moving to new locations.
Solution Approach 2:
The UE is empowered to autonomously activate and deactivate SCells based on event triggering conditions and monitoring data, without requiring continuous network control signaling. This self-service mechanism reduces signaling overhead and enables faster cell transitions while maintaining reliable connections.
2Productivity
If dense and ultra-dense cell deployment is implemented, then system throughput is improved, but mobility performance deteriorates due to short usable cell time
Solution Approach 1:
The network pre-configures multiple SCells in advance based on predicted UE movement patterns. When the UE moves to a new cell, it can immediately activate a pre-configured SCell without waiting for RRC reconfiguration, thereby extending the usable time of each cell and maintaining continuous high-speed connectivity in dense deployments.
Solution Approach 2:
The system dynamically adapts SCell configuration based on real-time monitoring data and UE mobility patterns. The network continuously updates predictions and reconfigures SCells as needed, enabling the system to optimize for both high throughput in dense deployments and fast mobility performance.
3Stability of the object's composition
If RRC signaling is used for cell reconfiguration, then connection stability is improved, but time loss increases during cell switching
Solution Approach 1:
The network performs RRC reconfiguration in advance by predicting future SCells and pre-configuring them with all necessary parameters. This eliminates the need for time-consuming RRC signaling during actual cell switching operations, as the UE can directly activate pre-configured SCells while maintaining connection stability through event-triggered conditions.
Solution Approach 2:
The network creates copies of SCell configuration data and stores them in the UE for multiple potential target cells. These pre-copied configurations can be instantly activated without additional signaling, reducing time loss while maintaining the stability ensured by network-controlled configuration.
Data Source
AI summary
A network node may predict, from user equipment, UE mobility attributes and/or state, likely target SCells and pre-configure the UE with information about those SCells, allowing quick deactivation and activation when the UE reports one of the predicted target SCells as being better than then the existing current SCell.