Terminal Handover Prediction for Ultra-Dense Network Reliability
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Solution Overview
Problem
In ultra-dense networks (UDNs), the small cell radius and denser cell deployment lead to ping-pong handover and wireless link connection failures, making mobility management unreliable due to overlapping cell coverage and dynamic signal quality, which existing network-controlled handover schemes cannot effectively address.
Innovation Solution
A cell handover method where a terminal determines whether to ignore a handover command based on predicted mobility and service characteristic parameters, such as traffic probability, delay, and communication performance parameters, using machine learning algorithms to assess the suitability of target cells and adapt handover decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network-controlled handover schemes are used in ultra-dense networks, then handover commands are executed based on current signal quality, but ping-pong handover and wireless link connection failures occur due to overlapping cell coverage and dynamic signal quality
Solution Approach 1:
The terminal performs preliminary actions by predicting future mobility and service characteristic parameters before executing handover. The terminal calculates predicted values of parameters such as user equipment mobility, traffic probability, and delay before the handover command is executed, allowing it to anticipate whether the handover will be successful rather than reacting to current signal conditions alone.
Solution Approach 2:
The terminal autonomously determines whether to ignore the handover command based on its own predicted parameters without requiring complex network-side calculations. The terminal serves itself by independently assessing whether predicted communication performance parameters indicate a successful handover, reducing the mobility management complexity on the network side while improving reliability.
2Productivity
If handover decisions are made based on current signal quality in dense networks, then handover commands are issued frequently, but this leads to ping-pong handover due to overlapping cell coverage
Solution Approach 1:
The terminal performs preliminary prediction of communication performance parameters before handover execution. By calculating predicted values of user equipment mobility, traffic probability, and delay in advance, the terminal can assess whether a handover will be stable before actually performing it, preventing ping-pong handovers caused by transient signal fluctuations in dense networks.
Solution Approach 2:
The handover decision mechanism dynamically adapts to changing network conditions by using predicted parameters that account for future signal quality and user mobility. Instead of relying solely on static current signal measurements, the system dynamically evaluates predicted communication performance to determine handover stability, allowing it to distinguish between temporary signal variations and genuine handover opportunities.
3Reliability
If terminals execute handover commands without prediction in ultra-dense networks, then handover processes are simple and fast, but wireless link connection failures occur due to inability to assess target cell suitability
Solution Approach 1:
The terminal performs preliminary calculations of predicted communication performance parameters including user equipment mobility, traffic probability, and delay before the handover is executed. This advance prediction allows the terminal to assess target cell suitability and avoid handovers that would result in wireless link connection failures, while the predicted values are computed efficiently to minimize additional decision time.
4Measurement precision
If machine learning algorithms are used to predict handover parameters, then handover decisions become more accurate, but computational complexity and processing requirements increase
Solution Approach 1:
The approach changes the parameters used for handover decisions from simple current signal quality measurements to predicted communication performance parameters. By predicting user equipment mobility, traffic probability, and delay, the system achieves more accurate handover parameter assessment. The prediction can be implemented with varying levels of complexity depending on the specific algorithm chosen, allowing flexibility in balancing accuracy against device complexity.
Data Source
AI summary
A cell handover method is provided. The method is performed by a terminal and includes determining whether to ignore a handover command of cell handover according to a predicted value of a predicted parameter, in which the predicted parameter comprises a mobility and service characteristic parameter and/or a communication performance characteristic parameter of the terminal after the terminal is handed over to a target cell.