Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach

TL;DR

This paper introduces an unsupervised manifold learning method to detect coverage holes in mmWave 5G networks, aiding network planning by identifying vulnerable areas caused by blockages.

Contribution

It applies uniform manifold approximation and projection to identify coverage holes without labeled data, enhancing coverage analysis in mmWave networks.

Findings

The method effectively visualizes coverage holes in low-dimensional embeddings.

It preserves local data structures to accurately detect coverage boundaries.

Results on DeepMIMO dataset validate the approach's effectiveness.

Abstract

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.