Millimeter-Wave Base Station Deployment Using the Scenario Sampling Approach
Millimeter-Wave Base Station Deployment Using the Scenario Sampling Approach
复制标题
采用情景抽样方法的毫米波基站部署
DOI：
10.1109/tvt.2020.3026216
复制
发表时间：
2020-09
影响因子：
6.8
通讯作者：
Miaomiao Dong;Taejoon Kim;Jingjin Wu;E. Wong
中科院分区：
文献类型：
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作者：
Miaomiao Dong;Taejoon Kim;Jingjin Wu;E. Wong
虽然泊松点过程（PPP）已被广泛用于在许多网络设计问题中对用户分布进行建模，但现有的挑战是它经常在小小区网络中显示不准确性。在本文中，而不是采用PPP，我们捕获的随机性的用户设备（UE）通过收集许多他们的实现。具体而言，我们专注于毫米波（毫米波）基站（BS）部署问题在城市的几何形状，基于应用的场景采样方法，以前介绍的大规模优化，定量采样的一部分UE实现。受场景采样的启发，提出了一个小规模的毫米波基站部署问题，并利用所提出的低复杂度迭代搜索算法求解该问题的最优解。分析保证指定的大多数链路质量约束的所需数量的样本。仿真结果验证了场景抽样理论和算法的有效性。
While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for large-scale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm.