Handoff Algorithm Based on Location Prediction in Ultra-dense Heterogeneous Wireless Network
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摘要:
在密集异构蜂窝网络和无线局域网络构成的超密集异构无线网络中, 变速移动的车辆终端会面临更加频繁的切换，导致用户服务质量(QoS)变差。该文针对上述问题，首先，利用高斯马尔可夫移动模型，预测车辆下一时刻的位置，筛选出满足终端服务质量的候选网络集，与当前的候选网络集做交运算，其次，当前接入网络不在交集中，则使用变步长的萤火虫算法寻找最佳网络；再次，对因预测误差导致的切换失效，则把终端用户迁移到宏蜂窝，以保证通信的持续性。仿真结果表明，在超密集异构无线网络中，使用该文所提算法能够减少乒乓切换等频繁切换现象，同时，提升了用户的服务质量和网络吞吐量。
Abstract:In the ultra-dense heterogeneous wireless network composed of heterogeneous cellular networks and wireless local area networks, vehicle terminals with variable speeds will face more frequent handovers, resulting in the deterioration of user’s Quality of Service (QoS). For the above problems, firstly, the Gauss Markov mobility model is used to predict the position of the vehicle terminal at the next moment, and the candidate network set that meets the terminal service quality is selected to make the intersection with the current candidate network set. Secondly, if the current access network is not in the intersection, the variable-step firefly algorithm is used to find the best network. Thirdly, the terminal that fails to switch due to the prediction error is migrated to the macro cellular to ensure the continuity of communication. Simulation results show that the proposed algorithm can reduce the frequent handoff phenomenon, such as ping pong handoff in the ultra-dense heterogeneous wireless network. Meanwhile, it can improve the user service quality and network throughput.
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Key words:
- Ultra-heterogeneous network /
- Handoff /
- Quality of Service (QoS) /
- Location prediction
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表 1 仿真参数的设置
网络 覆盖半径(km) 发送功率(dBm) 路径损耗因(dBm) 最大终端接入(个) 5G1 1.00 46 25 15 5G2 1.30 46 25 15 WLAN 0.50 35 35 12 micro 0.35 30 32 10 
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