In wireless propagation, the multipath arrivals of a transmitted signal appear clustered at the receiver. Because the notion of clusters tends to be intuitive rather than well-defined, cluster identification in channel modeling has traditionally been carried out through human visual inspection. Besides time-consuming for large-scale measurement campaigns, this approach is subjective and will vary from person to person, leading to arbitrary selection of clusters. In response to these concerns, automatic clustering algorithms have emerged in the past decade. Most, however, are laden with settings which depend on the radio-frequency environment under inspection, again leading to arbitrary selection. In this paper, we propose a novel clustering algorithm based on the kurtosis metric which, in related work, has been used precisely for its channel invariance. We compare it to two other algorithms through a standard validation method on simulated channel impulse responses from five different environments. The proposed algorithm delivers better results and, because it has only two settings which were maintained fixed across all environments, is proven robust to channel variance.
Proceedings Title: IEEE International Conference on Communications
Conference Dates: June 9-13, 2013
Conference Location: Budapest, -1
Pub Type: Conferences
Wireless, exponential, decay constant, Lognormal, Rayleigh