Angular Clustering of Millimeter-Wave Propagation Channels with Watershed Transformation

TL;DR

This paper introduces an image processing-based angular clustering method for millimeter-wave channels, utilizing watershed segmentation to improve accuracy and computational efficiency in channel characterization.

Contribution

It presents a novel unsupervised clustering technique using watershed transformation for 2D power angular spectrum maps in millimeter-wave channels.

Findings

Outperforms K-Power-Mean clustering in accuracy

Effective in simulation with IEEE 802.11ad model

Validated with indoor 60 GHz measurements

Abstract

An angular clustering method based on image processing is proposed in this paper. It is used to identify clusters in 2D representations of propagation channels. The approach uses operations such as watershed segmentation and is particularly well suited for clustering directional channels obtained by beam-steering at millimeter-wave. This situation occurs for instance with electronic beam-steering using analog antenna arrays during beam training process or during channel modeling measurements using either electronic or mechanical beam-steering. In particular, the proposed technique is used here to cluster two-dimensional power angular spectrum maps. The proposed clustering is unsupervised and is well suited to preserve the shape of clusters by considering the angular connection between neighbor samples, which is useful to obtain more accurate descriptions of channel angular properties. The approach is found to outperform approaches based on K-Power- Means in terms of accuracy as well as computational resource . The technique is assessed in simulation using IEEE 802.11ad channel model and in measurement using experiments conducted at 60 GHz in an indoor environment.