User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency
User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency
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DOI：
10.1109/access.2018.2820898
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发表时间：
2018-04
期刊：
影响因子：
3.9
通讯作者：
U. Hashmi;Syed Ali Raza Zaidi;A. Imran
中科院分区：
文献类型：
--
作者：
U. Hashmi;Syed Ali Raza Zaidi;A. Imran
在这篇文章中，我们开发了一个统计框架来量化以用户为中心的基于云的无线接入网络（UC-RAN）下行链路的区域频谱效率（ASE）和能效（EE）性能。我们提出了一个以用户为中心的远程无线电头端（RRH）集群机制，它：1）提供了显着的改善，通过选择多样性的接收信号干扰比; 2）通过诱导排斥之间的调度以用户为中心的RRH集群，使有效的干扰保护;和3）可以自组织的集群半径，以处理时空变化的用户密度。结果表明，在建议的以用户为中心的聚类机制，ASE（位/秒/赫兹/平方米）最大化在一个最佳的集群大小。据观察，这个集群的大小是敏感的RRH和用户密度的变化，因此，必须适应这些参数的变化。接下来，我们以功耗的形式来制定为UC-RAN容量增益支付的成本，然后将其转换为UC-RAN的EE（比特/秒/焦耳）。据观察，与产生最大ASE的集群半径相比，使UC-RAN的EE最大化的集群半径相对较大。因此，我们注意到UC-RAN的ASE和EE之间的权衡表现在集群半径选择方面。这种权衡可以通过利用简单的两个玩家合作游戏来利用。数值结果表明，从模型化的讨价还价问题的纳什讨价还价解决方案获得的最佳集群半径可以通过指数权重参数进行调整，该参数提供了一种机制，以利用UC-RAN中固有的ASE-EE权衡。此外，与现有的国家的最先进的非以用户为中心的网络模型相比，我们提出的方案，凭借选择性RRH激活和非重叠的以用户为中心的RRH集群，提供更高的和可调的系统ASE和EE，特别是在密集部署的情况下。
In this article, we develop a statistical framework to quantify the area spectral efficiency (ASE) and the energy efficiency (EE) performance of a user-centric cloud based radio access network (UC-RAN) downlink. We propose a user-centric remote radio head (RRH) clustering mechanism, which: 1) provides significant improvement in the received signal-to-interference-ratio through selection diversity; 2) enables efficient interference protection by inducing repulsion among scheduled user-centric RRH clusters; and 3) can self-organize the cluster radius to deal with spatio–temporal variations in user densities. It is shown that under the proposed user-centric clustering mechanism, the ASE (bits/s/Hz/m2) maximizes at an optimal cluster size. It is observed that this cluster size is sensitive to changes in both RRH and user densities and, hence, must be adapted with variations in these parameters. Next, we formulate the cost paid for the UC-RAN capacity gains in terms of power consumption, which is then translated into the EE (bits/s/Joule) of the UC-RAN. It is observed that the cluster radius which maximizes the EE of the UC-RAN is relatively larger as compared with that which yields maximum ASE. Consequently, we notice that the tradeoff between the ASE and the EE of UC-RAN manifests itself in terms of cluster radius selection. Such tradeoff can be exploited by leveraging a simple two player cooperative game. Numerical results show that the optimal cluster radius obtained from the Nash bargaining solution of the modeled bargaining problem may be adjusted through an exponential weightage parameter that offers a mechanism to utilize the inherent ASE-EE tradeoff in a UC-RAN. Furthermore, in comparison with existing state-of-the-art non user-centric network models, our proposed scheme, by virtue of selective RRH activation and non overlapping user-centric RRH clusters, offers higher and adjustable system ASE and EE, particularly in dense deployment scenarios.