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arXiv 1409.7822cs.ITmath.IT
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用于优化分布式天线位置的输入输出聚类准则(IOCC)

Input-Output Clustering Criterion (IOCC) for Optimizing Distributed Antenna Locations

Zekeriya Uykan, Riku Jantti

摘要:

本文提出了一种输入输出空间聚类准则(IOCC),用于在总功率约束下优化广义分布式天线系统(DAS)的远端天线单元(RAU)的位置。在IOCC中,输入空间是指RAU位置空间,输出空间是指用于噪声受限环境的位置特定遍历容量空间。给定一个特定位置的任意期望遍历容量函数在一个地理区域,我们定义的误差为实际和期望遍历容量之间的差异。我们的研究表明,i)IOCC提供了一个上限的细胞平均遍历容量误差;和ii)推导出的上限等于一个加权的量化误差函数在位置容量空间(输入输出空间)和iii)的上限可以任意小的聚类过程中增加的RAU的数量为一个可行的DAS。IOCC将RAU位置问题转换为输入-输出空间中矢量量化的码本设计问题,因此包括[15](和其他相关论文)中DAS的平方距离准则(SDC)作为特例,其仅考虑输入空间。计算机模拟证实了理论发现,并表明IOCC在定义的小区平均“有效”各态历经容量方面优于DAS的SDC。

英文摘要:

In this paper, we propose an input-output space clustering criterion (IOCC) to optimize the locations of the remote antenna units (RAUs) of generalized Distributed Antenna Systems (DASs) under sum power constraint. In IOCC, the input space refers to RAU location space and output space refers to location specific ergodic capacity space for noise-limited environments. Given a location-specific arbitrary desired ergodic capacity function over a geographical area, we define the error as the difference between actual and desired ergodic capacity. Our investigations show that i) the IOCC provides an upper bound to the cell averaged ergodic capacity error; and ii) the derived upper bound is equal to a weighted quantization error function in location-capacity space (input-output space) and iii) the upper bound can be made arbitrarily small by a clustering process increasing the number of RAUs for a feasible DAS. IOCC converts the RAU location problem into a codebook design problem in vector quantization in input-output space, and thus includes the Squared Distance Criterion (SDC) for DAS in [15] (and other related papers) as a special case, which takes only the input space into account. Computer simulations confirm the theoretical findings and show that the IOCC outperforms the SDC for DAS in terms of the defined cell averaged "effective" ergodic capacity.

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