Conditional Handover Trigger Parameter Adaptation
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Solution Overview
Problem
Conventional Conditional Handover (CHO) solutions in wireless communication fail to provide accurate handover parameters, leading to service interruptions due to dynamic changes in target cell conditions and lack of consideration for mobility parameters, resulting in radio link failures and incorrect cell handovers.
Innovation Solution
A system and method utilizing a network analysis-based Machine Learning model to determine handover trigger parameters for target cells, selecting the optimal cell based on signal strength, network parameters, historical data, and mobility parameters, ensuring seamless handovers and prioritizing service-supported cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CHO trigger configuration parameters are computed by the network and shared in advance with the UE, then the handover configuration is provided to UEs in advance, but the parameters are not accurate for dynamically changing conditions leading to radio link failures
Solution Approach 1:
The patent implements dynamic handover parameter adjustment by continuously monitoring mobility parameters (speed, direction) and signal conditions, then adapting CHO trigger configuration parameters in real-time. The network entity updates parameters based on current UE mobility state and target cell conditions, transforming static pre-configured parameters into dynamic adaptive parameters that maintain accuracy throughout the handover process.
Solution Approach 2:
The system establishes a feedback loop where the network entity receives measurement reports from the UE about signal conditions and mobility parameters, processes this information to determine optimal handover parameters, and sends updated CHO trigger configuration parameters back to the UE. This closed-loop feedback mechanism ensures parameters remain accurate despite changing conditions.
2Loss of time
If the network provides handover parameters in advance, then the CHO configuration is prepared ahead of time, but the parameters cannot account for dynamically changing target cell conditions and mobility parameters
Solution Approach 1:
The network entity performs preliminary actions by pre-identifying potential target cells and pre-configuring CHO parameters based on initial conditions. However, it also prepares the system for dynamic adaptation by establishing the feedback mechanism and parameter update procedures, allowing the pre-configured parameters to be adjusted when conditions change, thus combining advance preparation with adaptability.
Solution Approach 2:
The system transitions from static pre-configured parameters to dynamic parameters that adapt to changing mobility conditions and target cell states. The network entity continuously monitors UE mobility parameters and signal conditions, then dynamically updates CHO trigger configuration parameters to maintain optimality throughout the handover process.
3Adaptability or versatility
If conventional CHO evaluates multiple candidate cells, then more handover options are available, but the UE may handover to a wrong cell that does not support required services
Solution Approach 1:
The patent applies local quality by evaluating each candidate cell individually based on its specific service support capabilities. The network entity assesses which cells support the UE's required services (e.g., eMBB, uRLLC, mMTC) and assigns different evaluation weights or priorities to cells based on their local characteristics. This ensures that among multiple handover options, only cells with appropriate service support are selected.
Solution Approach 2:
The system uses feedback mechanisms where the network entity receives information about target cell service capabilities and UE service requirements, then uses this feedback to filter and prioritize candidate cells. The feedback loop ensures that handover decisions are made with knowledge of which cells can actually support the required services, preventing handovers to incompatible cells.
4Extent of automation
If the network makes CHO decisions based on measurement reports, then centralized control is maintained, but the network cannot provide ideal handover parameters for upcoming UE conditions
Solution Approach 1:
The patent strengthens the feedback loop by having the network entity continuously receive measurement reports from the UE including mobility parameters and signal conditions. The network processes this feedback information to dynamically determine and update optimal handover parameters, maintaining centralized control while improving parameter accuracy through real-time information exchange.
Solution Approach 2:
The system maintains centralized automated control while introducing dynamics through continuous parameter updates. The network entity automatically adjusts CHO trigger configuration parameters based on real-time feedback about UE conditions and target cell states, transforming static centralized control into dynamic adaptive centralized control that responds to changing conditions.
Data Source
AI summary
A method for performing a CHO includes receiving, via a network entity, a CHO reconfiguration associated with a plurality of target cells, identifying one or more target cells from among the plurality of target cells using the CHO reconfiguration, each of the one or more target cells having a higher signal strength than a serving cell associated with the UE, determining one or more handover trigger parameters associated with each of the one or more target cells using a network analysis-based Machine Learning model, the determining of the one or more handover trigger parameters being based on one or more network parameters, historical handover data, and one or more mobility parameters, selecting a first target cell from among the one or more target cells based on the one or more handover trigger parameters, and performing the CHO from the serving cell to the first target cell.