Angular Clustering of Millimeter-Wave Propagation Channels With Watershed Transformation

An angular clustering method based on image processing is proposed in this article. It is used to identify clusters in 2-D 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 channel modeling measurements using either electronic or mechanical beam steering. In particular, the proposed technique is used here to cluster 2-D 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 resources. The technique is assessed in simulation using the IEEE 802.11ad channel model and in measurement using experiments conducted at 60 GHz in an indoor environment.