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Physical Resource Block clustering method for an OFDMA cognitive femtocell system

机译：OFDMA认知毫微微小区系统的物理资源块聚类方法

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This paper deals with a heterogeneous Orthogonal Frequency Division Multiple Access (OFDMA) network where a user-deployed low power femtocell operates in the coverage area of a traditional macro-cell using the same frequency band. Femtocells represent a promising solution to increase the network capacity in next-generation wireless networks. However, the arising interference must be mitigated by means of suitable resource allocation policies. In particular, an OFDMA system allows a flexible usage of the resources that are organized in Physical Resource Blocks (PRBs). This flexibility can be fully exploited in a heterogeneous network if the femtocell base station knows how the PRBs are organized and grouped. This paper considers a cognitive femtocell base station that is able to sense the environment and identify the set of PRBs allocated to a given user by the macro-cell base station. A PRBs clustering method is proposed here. Initially, suitable inputs are derived and then provided to the K-means algorithm for a clustering refinement. The method proposed here is able to correctly gather together the PRBs of each user. Performance comparisons with a hierarchical clustering method is presented. The benefits of PRBs clustering on direction of arrival estimation are shown in order to prove the effectiveness of the proposed methods.
机译：本文研究一种异构正交频分多址（OFDMA）网络，其中用户部署的低功率毫微微小区在传统宏小区的覆盖区域内使用相同频带进行操作。毫微微小区是增加下一代无线网络的网络容量的有前途的解决方案。但是，必须通过适当的资源分配策略来减轻产生的干扰。特别是，OFDMA系统允许灵活使用物理资源块（PRB）中组织的资源。如果毫微微小区基站知道PRB的组织和分组方式，则可以在异构网络中充分利用这种灵活性。本文考虑了一种认知型毫微微小区基站，该基站能够感知环境并识别宏小区基站分配给给定用户的PRB集。本文提出了一种PRB聚类方法。最初，导出合适的输入，然后将其提供给K-means算法以进行聚类优化。这里提出的方法能够正确地收集每个用户的PRB。提出了使用分层聚类方法的性能比较。证明了PRB聚类对到达方向估计的好处，以证明所提出方法的有效性。

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