Abstract
Cluster tracking is a mainstream approach for the study of time-variant channel characteristics. In the paper, we propose a power spectrum based sequential tracker (PSBST) to compensate for the disadvantages of existing cluster tracking algorithms. The proposed tracker identifies clusters via simple three-stage power spectrum processing. Furthermore, fuzzy c-means (FCM) algorithm is incorporated to separate clusters considering the overlapped clusters which may appear in the power spectrum. In terms of tracking, we implement Kalman filter to sequentially predict candidate ranges of tracked clusters in consecutive snapshots and simultaneously a novel gradient-based histogram of power (GBHOP) method is devised to determine the evolution of clusters. We also investigate the performance of the tracker by synthetic channel simulation. It demonstrates high accuracy and less computational time compared with the results derived from the existing cluster tracking algorithms. Besides, we verify applicability of the tracker by analyzing the field measurement conducted in vehicle-to-everything (V2X) scenario, and preliminary statistical characteristics for intra-cluster and inter-cluster parameters can be readily obtained in the sequel.
Highlights
Radio propagation channel is the essential of wireless communications system, and channel characteristics influence the performance of the whole system significantly
It is found that multipath components (MPCs) usually appear in distinct clusters which are defined as groups of MPCs with similar parameters such as delay, Doppler frequency, angle of arrival (AoA), angle of departure (AoD) and so on in the channel [1]–[3]
In the paper, we present a novel power spectrum based cluster tracking method power spectrum based sequential tracker (PSBST) to analyze the characteristics of time-variant channel
Summary
Radio propagation channel is the essential of wireless communications system, and channel characteristics influence the performance of the whole system significantly. In order to avoid these troublesome details, a new kind of algorithms represented by the power-angle-spectrum based clustering and tracking algorithm (PASCT) [24] recently emerges which identifies clusters via image processing of power spectrum and tracks clusters using image feature matching This kind of algorithms is the application of computer vision in channel modeling. PSBST can sequentially identify and track clusters in the time-variant channel It needs less predefined parameters which increases stability of the tracker. It contributes to cluster characteristics analysis in the nonstationary channel, and avoids the redundancy of tracking of individual MPCs proposed in [14]–[17]. The accuracy and computational time of PSBST are compared with those of PASCT [24] and the conventional KPowerMeans based cluster tracking algorithm (KPMCT) [8].
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