超密集网络中基于轨迹预测的资源规划
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摘要
在超密集网络中,小区间干扰严重制约了小区边缘用户的性能体验以及网络吞吐量。利用用户轨迹预测和信号地图可获得用户未来的平均信道信息,从而为用户规划未来的传输资源,相对于非预测方法可以大幅度提升网络性能。现有对资源规划的研究大都考虑较为理想的假设,为了分析基于实际预测性能达到的资源规划性能增益,本文研究了超密集网络中的轨迹预测方法和基于实际轨迹预测性能的资源规划性能。仿真结果表明,所提出的轨迹预测方法能够满足资源规划的要求,所提出的资源规划策略在任意预测窗长度下都能达到较好的性能,当预测窗长为3分钟时,相对于非预测的干扰管理方法能够将用户满意率提高45%以上。
Inter-cell interference severely limits the network throughput and cell-edge user experience in ultra-dense networks. By using the predict average channel gains from the predicted trajectories and a radio map, with which future resources can be exploited to coordinate interference, balance the traffic load to provide evident performance gain over non-predictive counterparts. However, the existing resource planning studies mainly consider the ideal assumption of predictive information. To investigate the actual performance gain of the resource plan, we develop a trajectory prediction method and a resource planning scheme in ultra-dense networks. The simulation results show that the proposed trajectory prediction method can meet the requirement of resource planning. Furthermore, the proposed resource planning can work well under arbitrary prediction horizons. When the prediction horizon is 3 minutes, compared with the non-predictive interference management method, our resource planning can improve the user satisfaction rate by more than 45%.
Inter-cell interference severely limits the network throughput and cell-edge user experience in ultra-dense networks. By using the predict average channel gains from the predicted trajectories and a radio map, with which future resources can be exploited to coordinate interference, balance the traffic load to provide evident performance gain over non-predictive counterparts. However, the existing resource planning studies mainly consider the ideal assumption of predictive information. To investigate the actual performance gain of the resource plan, we develop a trajectory prediction method and a resource planning scheme in ultra-dense networks. The simulation results show that the proposed trajectory prediction method can meet the requirement of resource planning. Furthermore, the proposed resource planning can work well under arbitrary prediction horizons. When the prediction horizon is 3 minutes, compared with the non-predictive interference management method, our resource planning can improve the user satisfaction rate by more than 45%.
引文
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[3] Yao C,Yang C,Xiong Z.Energy-saving predictive resource planning and allocation[J].IEEE Trans.on Commun.,2016,64(12):5078-5095.
[4] Yao C,Guo J,Yang C.Achieving high throughput with predictive resource allocation[C]//IEEE GlobalSIP 2016.
[5] Guo K,Liu T,Yang C,et al.Interference coordination and resource allocation planning with predicted average channel gains for HetNets[J].IEEE Access,2018,6:60137- 60151.
[6] Atawia R,Hassanein H S,Abou-Zeid H,et al.Robust content delivery and uncertainty tracking in predictive wireless networks[J].IEEE Trans.on Wireless Commun.,2017,16(4):2327-2339.
[7] Atawia R,Hassanein H S,Ali N A,et al.Utilization of stochastic modelling for green predictive video delivery under network uncertainties[J].IEEE Trans on Green Commun.and Netw.,2018,2(2):556-569.
[8] Chen X,Meriaux F,Valentin S.Predicting a user’s next cell with supervised learning based on channel states[J].IEEE SPAWC 2013.
[9] Altché,Florent,Arnaud D L F.An LSTM network for highway trajectory prediction[C]//IEEE ITSC 2017.
[10] Zhang W,Liu Y,Liu T,et al.Trajectory prediction with recurrent neural networks for predictive resource allocation[C]//IEEE ICSP 2018.
[11] Bullen,P.S.Handbook of Means and Their Inequalities[J].2003,10.1007/978-94-017-0399- 4.
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[15] 3GPP TSG RAN.Further advancements for E-UTRA physical layer aspects[S].Tech.Rep.36.814 v9.0.0,2010.