Network Switching Algorithm Based on Mobile Trajectory Prediction in Ultra-dense Heterogeneous Wireless Networks
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摘要: 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。Abstract: 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.
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表 1 网络仿真参数的设置
网络类型 宏蜂窝 微蜂窝 无线局域网络 网络数量 23 240 80 覆盖半径(m) 1000 60 100 基站发送功率(dBm) 46 30 35 路径损耗因子(dB) 26 32 35 通信时延(ms) [1,20] [10,50] [40,100] 时延抖动(ms) [1,10] [2,20] [15,30] 带宽(MHz) [5,10] [10,30] [25,40] 发送天线增益(dBm) 11 9 5 工作频率(GHz) 3.5 3.5 2.5 
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表 2 3种移动模式下，典型用户移动预测精度统计
移动模式 指标 本文 NN ELM SVM 模式1 R2 0.99991 0.99405 0.99359 0.99178 RMSE 3.709 30.649 31.804 36.019 模式2 R2 0.99995 0.99989 0.99985 0.99990 RMSE 4.225 6.453 7.495 6.183 模式3 R2 0.99998 0.99988 0.99992 0.99992 RMSE 0.726 1.925 1.609 1.602
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表 3 不同模式不同算法网络切换仿真——各项指标统计
运动模式 切换算法 平均切换频率(次/s) 平均RSS(dBm) 平均时延(ms) 平均带宽(MHz) 平均传输速率(Mbs) 平均切换失败概率(%) 模式1 被动切换 0.033 –93.16 12.91 7.849 5.168 0.000 主动切换 0.044 –91.31 14.15 7.497 4.863 0.128 本文算法 0.085 –92.77 16.46 9.432 6.199 0.005 模式2 被动切换 0.003 –93.00 14.00 7.784 5.117 0 主动切换 0.007 –91.14 14.92 7.514 4.866 0.011 本文算法 0.013 –94.62 19.85 10.490 6.993 0 模式3 被动切换 0.001 –90.89 15.53 8.760 5.723 0 主动切换 0.005 –88.99 14.63 7.472 4.749 0 本文算法 0.002 –94.94 21.24 11.302 7.669 0
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表 4 不同模式不同算法网络切换仿真-累计在网时长统计(s)
运动类型 切换算法 累计在网时长 宏蜂窝 微蜂窝 无线局域网 沿道路
移动被动切换 100746 904 863 主动切换 101833 425 255 本文算法 84870 10918 6725 局部布朗
运动被动切换 1186323 8776 4901 主动切换 1195642 3000 1358 本文算法 861756 251519 86725 近乎静止 被动切换 1135743 49857 14400 主动切换 1199044 810 146 本文算法 841921 292531 65548
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