Adaptive Time-to-Trigger Parameter for Heterogeneous Network Handover
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Solution Overview
Problem
In heterogeneous wireless communication networks, existing mobility procedures face challenges in optimizing mobility parameters for various cell change types, leading to suboptimal performance in cell handovers and reselections due to differences in cell coverage sizes and types, which can result in connection losses and handover failures.
Innovation Solution
User Equipment (UE) adjusts mobility parameters based on cell change types by scaling parameters such as time-to-trigger, measurement report event trigger threshold, and cell reselection timer, and estimates its mobility state by counting cell changes differently based on cell types, while also providing speed information to the network to optimize handover procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a UE uses a fixed time-to-trigger parameter for handover execution in heterogeneous networks, then the mobility procedure is simple to implement, but the handover performance deteriorates due to varying cell coverage sizes and types
Solution Approach 1:
The patent applies dynamics by making the time-to-trigger parameter adaptive rather than fixed. The UE dynamically adjusts the TTT value based on the detected cell change type (macro-to-macro, macro-to-small, small-to-macro, small-to-small), allowing the handover parameter to respond to varying network conditions and cell characteristics, thereby improving handover success rate while maintaining manageable complexity through rule-based adjustment
Solution Approach 2:
The patent implements parameter changes by modifying the time-to-trigger value according to the cell change type. Different TTT parameters are assigned to different handover scenarios (e.g., longer TTT for macro-to-small cell handover, shorter TTT for small-to-macro cell handover), optimizing handover performance for each specific situation without requiring complete redesign of the mobility management system
2Productivity
If a UE scales mobility parameters based on cell change types, then the mobility procedure performance is improved, but the complexity of parameter determination increases
Solution Approach 1:
The patent applies segmentation by dividing the heterogeneous network into distinct cell types (macro cells and small cells) and defining specific handover scenarios based on source and target cell type combinations. This segmentation allows the UE to apply different mobility parameter scaling rules to each scenario, improving mobility procedure efficiency by tailoring parameters to specific network conditions while managing complexity through structured categorization
Solution Approach 2:
The patent implements local quality by applying different mobility parameter scaling factors to different cell change types. Each handover scenario (macro-to-macro, macro-to-small, small-to-macro, small-to-small) receives locally optimized parameters suited to its specific characteristics, such as coverage size differences and signal propagation conditions, thereby improving overall mobility efficiency without requiring uniform parameter adjustment across all scenarios
3Measurement precision
If a UE uses a uniform cell change counting method, then the mobility state estimation is simple, but the accuracy of mobility state estimation deteriorates in heterogeneous networks
Solution Approach 1:
The patent applies dynamics to mobility state estimation by making the cell change counting method adaptive to cell change types. The UE dynamically adjusts the counting weight or significance assigned to each cell change event based on whether it involves macro or small cells, improving measurement precision by reflecting the different mobility implications of various handover scenarios while managing complexity through systematic differentiation
Solution Approach 2:
The patent implements parameter changes in the cell change counting methodology by assigning different weights or counting values to different cell change types. This allows the mobility state estimation to account for the varying impact of handovers between different cell types, improving estimation accuracy without requiring complete redesign of the mobility state detection mechanism
Data Source
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AI summary
Systems and methods for mobility parameter adjustment and mobility state estimation in heterogeneous networks are provided. The mobility parameters may be adjusted based on the cell change types associated with the mobility procedure. The mobility procedure may be a cell handover procedure or a cell reselection procedure. The cell change type may be dependent on the transmission power level of the UE's serving cell and neighboring cells. In some implementations, the UE may provide a speed information to the serving cell such that the serving eNB may prioritize or optimize the mobility procedure for the UE. The UE may also estimate its mobility state by counting the number of cell changes within certain period of time and applying scaling factors to the number of cell changes based on the associated cell change types.