同构与异构网中预测资源分配的性能

张宸祚; 赵百川; 徐兆祺; 郭佳; 杨晨阳

doi:10.16798/j.issn.1003-0530.2019.10.004

同构与异构网中预测资源分配的性能

北京航空航天大学电子信息工程学院

基金项目: 教育部-中国移动科研基金项目资助（1-4 MCM2017）；国家自然科学基金重点项目资助（61731002）

详细信息

中图分类号: TN929.53
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出版历程
- 收稿日期: 2019-01-31
- 修回日期: 2019-03-13
- 发布日期: 2019-10-24

Performance of Predictive Resource Allocation in Homogeneous and Heterogeneous Networks

School of Electronics and Information Engineering, Beihang University

摘要

摘要: 预测资源分配能利用蜂窝网络的残余资源大大提升吞吐量。本文面向视频点播等非实时业务，研究在使95%用户播放视频的卡顿时间小于其预期值时预测资源分配能够使网络支持的非实时业务请求到达率提升多少。为了研究预测窗长对预测资源分配性能的影响，考虑一种性能接近最优解的低复杂度双门限策略，分析了预测窗长度、残余带宽、预测方法、用户接入和小区间干扰对其性能的影响。研究结果表明，通过对所需各种信息设计合理的预测方法，预测误差对双门限策略影响很小；预测窗越长，该策略相对于传统非预测方法的吞吐量增益越大、但增速随窗长增加逐渐变缓；网络残余带宽的方差越大，双门限策略相对于非预测方法的吞吐量增益越大；基于残余带宽的接入方法在异构网络中性能远优于基于接收功率最大的用户接入，且网络负载越重、增益越大。
- 预测资源分配 /
- 预测窗长 /
- 视频点播 /
- 用户接入 /
- 深度学习
Abstract: Predictive resource allocation can boost throughput remarkably by exploiting residual resource in cellular networks. In this paper, we investigate the performance of predictive resource allocation for non-real-time service such as video on demand in terms of boosting the maximal arrival rate when the stalling time during video playback of 95% users is less than the expected value of the users. To study the impact of duration of prediction window on the performance, we consider a two-threshold policy that is with low complexity but with performance very close to the optimal solution. We analyze the impact of prediction window duration, residual bandwidth, prediction methods，user association and inter-cell interference on its performance. Simulation results show that the two-threshold policy is robust to prediction errors if proper methods are designed for predicting the required information. The throughput gain of this policy over non-predictive method increases with the length of prediction window but the growing speed reduces gradually. When the variance of residual bandwidth is larger, the throughput gain of the policy over non-predictive method is greater. In heterogeneous networks, by using a residual-bandwidth based user association method, the two-threshold policy can achieve much better performance than using the maximal-receive-power based user association, and the throughput gain is higher when the traffic load is heavier.
- Predictive resource allocation /
- Duration of prediction window /
- Video on demand /
- User association /
- Deep learning

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参考文献(15)

[1]	Bui N, Widmer J. Data-driven evaluation of anticipatory networking in LTE networks[J]. IEEE Trans. Mobile Comput., 2018, 17(10):2252-2265.
[2]	Yao C, Yang C, Xiong Z. Energy-saving predictive resource planning and allocation[J]. IEEE Trans. on Commun., 2016, 64(12):5078-5095.
[3]	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., 2018:1-1.
[4]	Yao C, Yang C, Chih-Lin I. Data-driven resource allocation with traffic load prediction[J]. Journal of Communications and Information Networks, 2017, 2(1):52-65.
[5]	Guo K, Liu T, Yang C, Xiong Z. Interference coordination and resource allocation planning with predicted average channel gains for HetNets[J]. IEEE Access, 2018, 6:60137-60151.
[6]	Zhang W, Liu Y, Liu T, et al. Trajectory prediction with recurrent neural networks for predictive resource allocation[C]// IEEE ICSP 2018, 2018: 1-6.
[7]	Chen, J, Yatnalli U, and Gesbert, D. Learning radio maps for UAV-aided wireless networks: A segmented regression approach[C]//IEEE ICC 2017 1-6.
[8]	Wang J, Tang J, Xu Z, et al. Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach[C]//IEEE INFOCOM 2017, 2017: 1-9.
[9]	Guo J, She C, Yang C. Predictive Resource Allocation with Coarse-Grained Mobility Pattern and Traffic Load Information[C]// IEEE ICC 2018, 2018 1-6.
[10]	Guo J, Yang C, Chih-Lin I. Exploiting Future Radio Resources with End-to-end Prediction by Deep Learning[J]. IEEE Access, 2018, 6(1): 75729-75747
[11]	Helmy, Amir, Le-Ngoc T, et al. Energy-Efficient Power Adaptation over a Frequency-Selective Fading;Channel with Delay and Power Constraints[J]. IEEE Trans. Wireless Commun., 2013, 12(9):4529-4541.
[12]	She C, Yang C, Liu L. Energy-Efficient Resource Allocation for MIMO-OFDM Systems Serving Random Sources With Statistical QoS Requirement[J]. IEEE Trans. Commun., 2015, 63(11):4125-4141.
[13]	Su D, Yang C. User-Centric Downlink Cooperative Transmission With Orthogonal Beamforming Based Limited Feedback[J]. IEEE Trans. Commun., 2015, 63(8):2996-3007.
[14]	Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.
[15]	3GPP TSG RAN. Further advancements for E-UTRA physical layer aspects [S]. Tech. Rep. 36.814 v9.0.0, 2010.