超密集网络中的绿色预测资源分配

发布时间：2018-05-29 00:07

本文选题：预测资源分配 + 大数据　；参考：《信号处理》2017年04期

【摘要】：大数据分析兴起使得系统可以预测用户的移动轨迹和业务需求等信息,从而可以根据预测信息对资源进行预先分配,在满足用户需求的同时降低网络的资源消耗。相比于无干扰网络,在基站密集部署的网络中,干扰的存在使得用户数据率预测与资源分配耦合,增加了干扰网络中进行预测资源分配的复杂性。本文研究了在保证用户业务需求情况下如何最小化系统资源消耗的问题,提出了一种能够有效协调网络干扰的预测资源分配方法。仿真结果表明,本方法基于可预测的大尺度信道信息进行预测资源分配,能够在相同的用户需求下提高网络成功传输率,降低系统能量资源消耗,提高资源的频谱效率。
[Abstract]:The rise of big data analysis enables the system to predict the mobile trajectory and business requirements of users, so that the resources can be pre-allocated according to the prediction information, and the resource consumption of the network can be reduced while meeting the needs of users. Compared with the non-interference network, in the network with dense deployment of base stations, the presence of interference makes the user data rate prediction coupled with resource allocation, which increases the complexity of prediction resource allocation in the interference network. In this paper, the problem of how to minimize the system resource consumption under the condition of guaranteeing the user's business requirements is studied, and a predictive resource allocation method which can effectively coordinate network interference is proposed. The simulation results show that the proposed method can improve the successful transmission rate of the network, reduce the energy consumption of the system and improve the spectral efficiency of the network under the same user demand by using the predictive resource allocation based on the predictable large-scale channel information.
【作者单位】：北京航空航天大学电子信息工程学院;中国移动通信有限公司研究院;
【基金】：国家自然科学基金(61301085);国家自然科学基金海外及港澳学者合作研究基金(61429101) 中国移动研究院资助项目(2015463)
【分类号】：TN929.5

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