  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.
