Machine Learning-Based Handoff Method in UDN

Abstract

The invention discloses a handover method based on machine learning in UDN, which includes the following steps: Step A: Every time a mobile device needs to switch to a new micro cell, it first reports the characteristic information report of the location of the mobile device to the macro base station , the macro base station discretizes the feature information; Step B: use four learners, machine learning decision tree prediction, neural network prediction, SVM prediction and random forest prediction, to train the discretized feature information data respectively, and obtain each training Model; Step C: Make a decision based on the relative majority voting method for each prediction result, and obtain the decision result; Step D: When the decision result is that the mobile device needs to be handed over and the pre-handover condition is met, the macro base station will go to the target of the pre-handover The micro cell sends a pre-handover request, and the target micro cell starts to prepare resources for the mobile device at this time, and implements the handover. It reduces unnecessary switching and reduces the average delay of the system in the ultra-dense network.

Description

technical field [0001] The invention belongs to the technical field of communication networks, and in particular relates to a switching method based on machine learning in UDN. Background technique [0002] In order to cope with the thousands-fold increase in the data volume of the next-generation wireless communication network, Ultra Dense Network (UDN) is considered to be one of the most promising key technologies and an inevitable trend of future mobile communication. In the UDN, the mobile device constantly switches between cells, resulting in a relatively large switching delay, which brings a large burden to the system. The prediction of user switching can improve the performance of switching. However, for the traditional prediction method based on linear programming, the prediction accuracy is not enough, especially in the face of increasingly dense and complex networks. Not enough to make a positive impact on the performance improvement of switching. As for the pred...

AI Technical Summary

Problems solved by technology

[0005] In addition, although a simple machine learning algorithm such as SVM can predict the mobility of terminals relatively accurately in real time, there are often many features that affect user mobility. In the case of multiple features, SVM often cannot obtain a better performance

In the UDN network, facing dense base stations and frequent handovers often require more complex user information characteristics, and the handover prediction made by a single algorithm cannot accurately

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