Method for selecting handover trigger time of user based on auto-regressive (AR) model prediction

Abstract

The invention discloses a method for selecting handover trigger time of a user based on auto-regressive (AR) model prediction, which is mainly used for solving the problem of handover failure caused by too early or too late handover trigger of a user in the existing method. The method comprises the following steps: the user initiates a handover request, and then a terminal starts to monitor the received signal strength of a target network (TN) for handover; the terminal predicts the received signal strength of the TN for handover by virtue of an AR model, and determines the earliest limit T1 of the handover trigger time of the user; the terminal carries out iterative prediction on the received signal strength of the current service network by virtue of the AR model, and determines the latest limit T2 of the handover trigger time of the user; the optimum handover trigger time T is determined according to the earliest limit T1 and the latest limit T2 of the predicted handover trigger time; and finally the user starts to execute a handover flow at the optimum handover trigger time. By means of the method, the optimum handover trigger time can be selected for the user, and the probability of handover failure can be effectively reduced, thus the method is suitable for the mobile user to select the trigger time for handover among heterogeneous networks.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to a method for selecting a handover trigger time during user movement under a cognitive wireless network environment, which can be used for handover trigger time selection of a user under heterogeneous network conditions. Background technique [0002] In recent years, the wireless communication industry has been greatly developed, and the emergence of various wireless access technologies has formed a complex heterogeneous wireless network environment. At the same time, people have higher and higher requirements on the service quality of wireless services, hoping to obtain network services anytime and anywhere. However, because different access networks work in different frequency bands, the coverage ranges are also different. For example, WLAN networks only cover certain hotspot areas in cities, WiMAX networks only achieve coverage in certain core areas of cities, and GSM / The...

AI Technical Summary

Problems solved by technology

However, this algorithm only takes into account the signal strength information received by the user terminal from the current service network, and does not take into account the status of the handover target network

Therefore, this method can only reduce the impact caused by late handover to a certain extent, and cannot achieve the goal of finding the best handover trigger time for users

[0005] In addition, the existing handover trigger time selection methods do not consider the handover trigger time selection problem under the condition of network coverage changes, resulting in poor adaptability of the method. When the overlapping coverage area between the current serving network and the handover target network is small, the user The probability of switching failure is high

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