Dynamic Handover Threshold Adjustment for Wireless Cell Load
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless networks face inefficiencies in handover processes between cells due to static handover thresholds that do not account for dynamic load and signal quality metrics, leading to suboptimal user experience and network performance.
Innovation Solution
Implementing dynamic adjustment of handover thresholds based on load metrics and signal quality metrics, utilizing AI/ML techniques to refine these thresholds for optimal UE performance and network efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static handover thresholds are used, then device complexity is reduced, but handover reliability deteriorates due to inability to adapt to dynamic network conditions
Solution Approach 1:
The patent implements dynamic handover thresholds that automatically adjust based on real-time cell load conditions. The network device determines handover thresholds dynamically according to the load state of the target cell, transforming the static threshold into a dynamic parameter that adapts to changing network conditions, thereby improving handover reliability without requiring complex manual intervention
Solution Approach 2:
The system employs self-service mechanisms where the network device autonomously monitors cell load metrics and automatically adjusts handover thresholds without external intervention. The threshold adjustment is performed based on pre-configured load conditions and measurement results, enabling the system to self-optimize handover parameters in response to dynamic network states
2Reliability
If dynamic handover thresholds based on real-time metrics are implemented, then handover reliability improves, but device complexity increases due to additional monitoring and adjustment mechanisms
Solution Approach 1:
The patent changes the parameter state of handover thresholds from fixed to variable, where the threshold value is adjusted according to cell load metrics. The network device determines different threshold values based on load conditions (e.g., high load, medium load, low load states), allowing the system to adapt to dynamic conditions while using a manageable set of discrete parameter states
Solution Approach 2:
The system implements feedback mechanisms where the network device continuously monitors cell load metrics and uses this information to adjust handover thresholds. The feedback loop compares actual load conditions against threshold criteria and automatically modifies handover decisions accordingly, improving reliability through closed-loop control without requiring overly complex systems
3Productivity
If AI/ML techniques are used to refine handover thresholds, then productivity of network optimization improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring load threshold criteria and handover parameter settings before dynamic adjustment is needed. The network device is pre-programmed with load condition thresholds and corresponding handover parameter adjustments, allowing it to quickly respond to changing conditions using pre-computed optimization rules rather than performing complex real-time AI/ML calculations
Data Source
AI summary
A system described herein may receive respective measures of load associated with first and second cells of a wireless network. The system may receive a measures of utilization associated with the first and second cells, as determined by a User Equipment (“UE”) that is connected to the first cell. The system may determine whether the UE should be handed over from the first cell to the second cell, and further based on the measures of load associated with the first and second cells, the measures of utilization associated with the first and second cells, as determined by the UE. The system may cause the UE to be handed over from the first cell to the second cell based on the determining.