超密集网络中的绿色预测资源分配

Green Predictive Resource Allocation for Ultra Dense Networks (UDNs)

  • 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: With the development of big data analyzing technology, the network could predict users’ trajectories and demands and then allocate system resource in advance based on the predictive information to reduce the cost while meeting users’ demands. While in ultra-dense networks (UDNs) users’ data rate prediction and resource allocation are coupled because of the interferences, which bring significant difficulty to design predictive resource allocation. This article focuses on how to minimize the system cost when all the users’ demands are satisfied and proposes a predictive resource allocation method that can coordinate interferences in UDNs effectively. The simulation result shows that allocating resource in advance based on predictable large-scale channel can improve the transmission success rate, reduce the cost and boost spectrum efficiency significantly, compared with the method without using the predictive information.

     

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