基于移动用户位置预测的垂直切换算法

针对异构无线网络环境中现有垂直切换算法存在难以实现移动用户接入网络的无缝切换，无法向用户提供稳定通信服务的问题，提出一种基于移动用户位置预测的垂直切换算法。首先利用用户移动轨迹的相似性，使用用户轨迹数据离线训练LSTM模型以学习各用户普遍具有的移动规律；然后在线加载LSTM模型进行用户位置预测，从而使用模糊逻辑分析计算下一时刻用户对应候选网络的回报值，并选取回报值最高的网络进行切换。实验结果表明，该算法与已有垂直切换算法相比，在不同的用户移动速度和用户规模条件下的切换次数、切换失败次数和切换时延均有明显下降，内存消耗较低，能够实现无缝切换。

Vertical handoff algorithm based on mobile user location prediction

Most existing vertical handoff algorithms cannot realize seamless handoff and provide stable communication services for mobile users in heterogeneous wireless networks. In order to solve the above problems, this paper proposed a vertical handoff algorithm based on mobile user location prediction. Firstly, using the similar regular characteristics of the mobile trajectories of each user, the algorithm trained the long short term memory(LSTM) model offline and used it to predict the location of each mobile user at the next moment online, and estimated the multi-attribute parameters of the future networks. After that, the algorithm used fuzzy logic to analyze the return set of all optional networks under the condition of multi-attribute parameters, and selected the network with the highest return in order to complete the handoff decision. The experimental results show that the proposed algorithm has better handover performance than the existing handover algorithms in terms of the number of handoff, the number of handoff failures, switching time and memory resource consumption under different mobile speeds and the scale of users. In conclusion, it can realize seamless handoff in heterogeneous wireless networks.