The invention discloses a
switching algorithm based on
machine learning in a UDN, and the
algorithm comprises the following steps of A when a
mobile device needs to be switched to a new
micro cell each time, firstly reporting a feature information report of the position where the
mobile device is located to a
macro base station, and discretizing the feature information by the
macro base station; Brespectively training the discretized feature
information data by adopting four learners of
machine learning
decision tree prediction, neural network prediction, SVM prediction and
random forest prediction to obtain each training model; C making a decision on each prediction result by using a majority voting method to obtain a decision result; and D when the decision result is that the
mobile device needs to be switched and a pre-switching condition is met, using the
macro base station to send a pre-switching request to a pre-switched target
micro cell, and using the target
micro cell to start to prepare resources for the mobile device and implement switching. According to the present invention, the unnecessary switching is reduced, and the average time
delay of the
system in the ultra-dense network is reduced.