A Novel Target Recognition Based Radio Channel Clustering Algorithm

In this paper, a novel target recognition based clustering algorithm is proposed for time-varying channels. Power angle spectrum (PAS) is extracted from measurement data by using Bartlett beamformer. Then the clusters in the PAS are separated from the background by using the proposed algorithm, where the amplitude distribution of the elements in the PAS is considered. Moreover, morphology operations are applied to further divide the clusters which are connected to each other. It is found that, the dominating clusters in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments can be well recognized by the proposed algorithm with low computation cost. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurement can be clearly observed, without using any high-resolution parameter estimation.

I. Introduction

Channel modeling plays an important role in radio communications, since the performance of any practical system depends on channel characteristics. Recently, many radio channel models are based on the concept of clustered mutipath components (MPCs), such as the Saleh-Valenzuela model [2], COST 2100 [3] and 3GPP Spatial Channel Model [4], etc., where the cluster has been usually considered as a group of MPCs with similar channel parameters [5], [6]. Meanwhile, radio channels are generally time-varying due to the movements of scatterers and/or communication terminals, which cause significant challenges in time-varying channel characterizations.