Vertical Handoff Method for Cognitive Heterogeneous Wireless Networks

Abstract

The present invention claims to protect a vertical handover method for cognitive heterogeneous wireless networks. In cognitive heterogeneous wireless networks, cognitive radio networks use spectrum opportunistically, which makes the problem of frequent handover more prominent. Aiming at this problem, this paper proposes a network selection algorithm based on grey relational analysis and bipartite maximum matching. On the user side, the grey relational analysis method is used to rank cognitive heterogeneous wireless networks; on the network side, the maximum matching algorithm of bipartite graph is used to achieve the optimal matching between users and the network. Simulation results show that the algorithm can effectively control frequent handovers, improve network throughput and user service quality, and achieve load balancing.

Description

technical field [0001] The invention belongs to a vertical handover method in a cognitive heterogeneous wireless network, and belongs to the field of mobile communication. In particular, it relates to a method for vertical switching using a bipartite graph maximum matching algorithm. Background technique [0002] At present, with the rapid development of mobile devices and wireless network technology, the future wireless network will be a heterogeneous wireless network composed of multiple wireless access technologies. In this complex heterogeneous wireless network environment, the handover between different access points is vertical handover. In the process of vertical handover, how to control frequent handovers and ensure user service quality has become one of the hot issues in academic research. [0003] At present, there have been many researches on vertical switching technology at home and abroad. According to the existing research methods, the vertical switching algo...

AI Technical Summary

Problems solved by technology

Among these algorithms, the algorithms based on RSS and MADM are easy to implement, but the former is easy to cause ping-pong switching, while the latter is less flexible

For algorithms based on fuzzy logic, it is difficult to determine the parameter weights, and at the same time, there is a problem of high time complexity

Although the algorithm based on neural network has high accuracy, its time complexity is too large

[0004] In addition, the above algorithms all consider network selection and switching from both directions of the user end and the network end at the same time, but only consider heterogeneous wireless networks composed of cellular networks, wireless local area networks, wireless wide area networks, etc. low quality problem

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