基于网格亲和传播的虚拟小蜂窝毫米波D2D UE聚类

Achmad Rizal Danisya, G. Hendrantoro, P. Handayani
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引用次数: 0

摘要

本文提出了网格辅助亲和传播聚类(GAPC)算法,通过增加Cell Head (CH)的数量来提高基于Cell Head的虚拟小Cell (CHVSC)业务的总频谱效率。该算法在改进的关联传播聚类(MAPC)方法的基础上,增加了网格分区和深度优先搜索算法,用于高级的合格ue选择。然后在GAPC-SNR和GAPC-SIR中分别使用信噪比和SIR进行成员选择。从蒙特卡罗仿真来看,MAPC算法的平均信噪比仍然高于GAPC- snr,但GAPC算法在CH指定数量上优于MAPC算法。与MAPC相比,GAPC-SNR在补偿MBS服务区内更高的聚类查找精度的同时,提高了整体带宽效率和轮廓评分,降低了计算复杂度,减轻了每个CH的流量负担。
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UE Clustering Based on Grid Affinity Propagation for mmWave D2D in Virtual Small Cells
In this paper, Grid Assisted Affinity Propagation Clustering (GAPC) algorithm is proposed to enhance the total spectral efficiency of Cell Head based Virtual Small Cell (CHVSC) service by increasing the number of Cell Heads (CH). The algorithm builds upon the previous method of Modified Affinity Propagation Clustering (MAPC) with addition of grid zonal division and Depth-First Search algorithm for advanced eligible-UE selection. Afterwards, both SNR and SIR are used for member selection in GAPC-SNR and GAPC-SIR respectively. From Monte Carlo simulation, MAPC still have higher average SINR compared to GAPC-SNR, but GAPC algorithm outperforms the MAPC algorithm in the number of CH appointed. With the compensation of higher accuracy of cluster finding inside MBS service zone, GAPC-SNR enhances overall bandwidth efficiency, silhouette score, and reduces computational complexity, as well as alleviating traffic burdens for each CH in comparison to MAPC.
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