超密集网络中最大化网络吞吐量的预测资源分配

发布时间：2018-04-05 20:22

本文选题：预测资源分配　切入点：大数据　出处：《信号处理》2017年03期

【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。无线大数据分析的飞速发展,使得人们有可能通过预测未来的信道状态来分配资源,在无干扰网络中可达到很大的性能增益。但是在干扰网络中如何利用预测信息,在分配资源的同时有效协调干扰还是一个尚未研究的问题。本文分析了干扰网络中预测资源分配的设计难点和存在的问题,针对该问题提出了相应的解决方法,将资源分配建模成一个凸优化问题,通过求解优化问题得到最优的资源分配方法。仿真表明,与未知预测信息的最大化网络吞吐量方法相比,所提方法能够有效提高用户的成功传输率、平均传输进度和网络的吞吐量。当用户数据需求较大时,所提方法可以提供较大的网络性能增益。
[Abstract]:In ultra-dense networks, inter-cell interference severely restricts the performance experience and network throughput of cell edge users.With the rapid development of wireless big data analysis, it is possible to allocate resources by predicting the future channel state, which can achieve a great performance gain in non-interference networks.However, how to use prediction information in jamming networks and coordinate interference effectively while allocating resources is still a problem that has not been studied.In this paper, the difficulties and problems in the design of predictive resource allocation in interference networks are analyzed, and a corresponding solution to this problem is put forward. The resource allocation is modeled as a convex optimization problem.The optimal resource allocation method is obtained by solving the optimization problem.Simulation results show that the proposed method can effectively improve the successful transmission rate, average transmission schedule and network throughput of users compared with the method of maximizing network throughput with unknown predictive information.When the user data demand is large, the proposed method can provide a large network performance gain.
【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研究基金(61429101) 中国移动研究院自助项目(2015463)
【分类号】：TN929.5

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