Machine Learning Handover Decisions for Low-Latency Wireless Connections
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Solution Overview
Problem
Existing wireless communications networks face challenges in efficiently handling handovers for a diverse range of devices with varying data traffic profiles, particularly in scenarios requiring high reliability and low latency, as conventional handover decisions are made solely by network infrastructure and involve significant signaling and potential delays.
Innovation Solution
A method utilizing machine learning to determine handovers based on input parameters, allowing communications devices to dynamically decide when to switch cells, reducing signaling requirements and improving handover accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional handover decisions are made solely by network infrastructure, then network control is maintained, but signaling overhead increases and handover delays occur
Solution Approach 1:
The communications device autonomously determines handover decisions using a machine learning model without requiring continuous network infrastructure intervention. The device independently evaluates input parameters, processes them through the trained model, and determines handover execution, thereby reducing signaling overhead and acceleration the handover process while maintaining reliable connectivity
2Reliability
If conventional handover decisions are made solely by network infrastructure, then centralized control is maintained, but signaling overhead increases
Solution Approach 1:
The communications device autonomously determines handover decisions using a machine learning model without requiring continuous network infrastructure intervention. The device independently evaluates input parameters, processes them through the trained model, and determines handover execution, thereby reducing signaling overhead and acceleration the handover process while maintaining reliable connectivity
3Measurement precision
If machine learning model is used for handover determination, then handover accuracy is improved, but device complexity increases
Solution Approach 1:
The machine learning model is trained in advance using historical handover data and performance metrics before deployment. This preliminary training phase allows the model to learn optimal handover decision patterns offline, so that during actual operation, the device only needs to evaluate current input parameters against the pre-trained model, reducing real-time computational complexity while maintaining high decision accuracy
Solution Approach 2:
The system transforms complex handover decision-making into a parameter-based evaluation process. The machine learning model accepts specific input parameters (signal strength, quality metrics, mobility information) and produces handover decisions based on learned patterns. This parameter transformation approach simplifies the decision process while improving accuracy compared to conventional threshold-based methods
Data Source
AI summary
A method of transmitting or receiving data by a communications device in a wireless communications network, the method comprising: establishing a connection for transmitting or receiving the data in a first cell of the wireless communications network, determining a value of one or more input parameters, using the value of the one or more input parameters as inputs to a model trained using machine learning, determining, based on an output of the model, that the communications device should perform a handover to establish a connection in a second cell, and responsive to determining that the communications device should establish a connection in the second cell, transmitting a handover message to request the establishment of a connection in a second cell.