The utilization of millimeter-wave (mmWave) bands in 5G networks poses new
challenges to network planning. Vulnerability to blockages at mmWave bands can
cause coverage holes (CHs) in the radio environment, leading to radio link
failure when a user enters these CHs. Detection of the CHs carries critical
importance so that necessary remedies can be introduced to improve coverage. In
this letter, we propose a novel approach to identify the CHs in an unsupervised
fashion using a state-of-the-art manifold learning technique: uniform manifold
approximation and projection. The key idea is to preserve the
local-connectedness structure inherent in the collected unlabelled channel
samples, such that the CHs from the service area are detectable. Our results on
the DeepMIMO dataset scenario demonstrate that the proposed method can learn
the structure within the data samples and provide visual holes in the
low-dimensional embedding while preserving the CH boundaries. Once the CH
boundary is determined in the low-dimensional embedding, channel-based
localization techniques can be applied to these samples to obtain the
geographical boundaries of the CHs.

本文提出了一种使用流形学习技术 —— 统一流形逼近和投影，无监督地识别覆盖漏洞的方法，利用该方法，可以通过区分深度学习数据中可视漏洞来确定覆盖洞边界，并应用基于通道的定位技术来获取覆盖洞的地理边界。