超密集异构无线网络中基于移动轨迹预测的网络切换算法

随着５G技术的广泛应用，网络超密集化部署已成为必然趋势。超密集异构无线网络在实现网络高流量密度、高峰值速率性能的同时，给传统的网络切换算法带来了挑战，处于变速移动的终端会面临更频繁的切换问题，这将导致乒乓效应频率的显著提高，进而影响用户在网体验。针对上述问题，该文提出一种基于终端移动轨迹预测的网络切换算法，适用于各类型用户在高密度无线网络中的垂直切换和水平切换问题。首先，为了更高精度的移动轨迹预测，提出一种基于模糊核聚类和长短期记忆(LSTM)神经网络的预测方法，可以有效预测不同移动模式下用户终端的短时移动轨迹；之后，基于用户当前和预测位置，获取候选网络集合，通过候选集交运算法和指标阈值判断网络切换时机；当切换触发时，使用帝企鹅算法最优化网络选择。仿真结果表明，相比于其他类型的时间序列预测算法，该文提出的轨迹预测算法精度较高；同时相较对比算法，该文所提网络切换算法的切换次数适中，有效避免了乒乓效应，且提高了用户连接的网络质量。

Network Switching Algorithm Based on Mobile Trajectory Prediction in Ultra-dense Heterogeneous Wireless Networks

With the widespread adoption of 5G technology, network hyper-density deployment has become an inevitable trend. While achieving high traffic density and high peak rate performance, ultra-dense heterogeneous wireless networks pose challenges to traditional network switching algorithms. Terminals moving at variable speeds will face more frequent switching problems, which will lead to a much higher frequency of ping-pong effects, thus affecting the user experience of the network. To address these issues, a network switching algorithm based on terminal trajectory prediction is proposed, which is applicable to both vertical and horizontal switching for all types of users in high-density wireless networks. Firstly, to predict the mobile trajectory more accurately, a prediction method based on fuzzy kernel clustering and Long Short-Term Memory (LSTM) neural networks is proposed, which can effectively predict the short-term mobile trajectory of user terminals under different mobile modes. Afterwards, two sets of candidate networks are obtained based on the current and predicted positions of the user, and the network switching timing is judged by the candidate set swapping algorithm and indicator threshold; When the switching is triggered, the emperor penguin algorithm is used to select optimally the network at the time of switching. The simulation results show that the trajectory prediction algorithm proposed has higher accuracy compared to other types of time series prediction algorithms. At the same time, compared with the comparison algorithms, the proposed network switching algorithm has a moderate number of switches, which avoids effectively the ping-pong effect and improves the network quality of user connections.