Multi-objective mobility management method based on contextual awareness

Abstract

The invention belongs to the field of a mobile communication network and introduces the concept of handover loss in order to propose a multi-objective mobility management method in mobile communication which strictly controls the occurrence of handover, reduces the handover frequency, solves an FHO problem in an ultra dense network and evaluates the advantages and disadvantages of a base station according to the user satisfaction and the number of handover times. Therefore, the technical scheme adopted by the invention is that the multi-objective mobility management method based on a contextual awareness-learning algorithm decides the base station handover sequence according to the collected contextual competitive score (CCS) by using a non-random learning model improved by a contextual awareness-learning (CA-L) algorithm, a small base station (SBS) with the maximum probability is calculated, and the concept of handover loss is introduced, thereby strictly controlling the occurrence of handover, and enabling the handover frequency to be reduced. The multi-objective mobility management method is mainly applied to wireless communication occasions.

Description

technical field [0001] The invention relates to the field of mobile communication networks, and in particular, relates to a multi-object mobility management method based on environment perception. Background technique [0002] Small base stations (Small Base Stations, SBSs) are deployed ultra-densely to form ultra-dense cells (SmallCells, SCells). This integrated technology is the most promising frontier. Spectrum reuse and improved spectrum efficiency drive the growth of wireless network capacity. However, the development of ultra-dense cell integration has brought new challenges to the design of wireless networks. Among these challenges, mobility management has become a bottleneck that affects the performance of the entire system. The fifth-generation mobile communication network has been put on the agenda, and ultra-dense cell deployment and software-defined network are two key drivers, while bringing new challenges to mobility management issues. The ultra-dense node dep...

AI Technical Summary

Problems solved by technology

The ultra-dense node deployment makes the problem more complicated, because in the ultra-dense network (Ultra DenseNetwork, UDN), the user equipment (User Equipment, UE) can have many serving cells at the same time, even if there is not much physical movement, when the UE moves It is also easy to trigger very frequent switching

Frequent handover (FHO) and ping-pong phenomenon (PP) are more likely to occur in ultra-dense networks. Most of the current solutions are to improve the original handover algorithm to adapt to the complexity of ultra-dense networks, but most of the solutions algorithm complexity Higher and the optimization content is relatively simple

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