A Novel Power Spectrum-Based Sequential Tracker for Time-Variant Radio Propagation Channel

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.INDEX TERMS Time-variant radio channel, cluster tracking, power spectrum, fuzzy clustering, cluster detection, V2X.

Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization

“…The PASCT algorithm identifies the clusters directly from the PAS, which can be fast obtained by applying the Bartlettbeamformer [120]. A similar method is also adopted in [112], where the cluster is recognized from the PAS by using image denoising, coarse-grained segmentation, and fine-grained segmentation. approach and thus can provide identification results that conform to human observation and benefit from the rapid development of computer vision science.…”

“…A cluster centroid tracking algorithm based on the Kalman filter is firstly proposed in [140] for the MIMO channels, where the cluster centroid is predicted and used as the initial position for the KPM algorithm. Similar solutions are also adopted in [112], [141]. The Kalman filter is designed to predict one MPC/cluster; alternatively, the particle filter [142] is developed to track multiple targets at the same time.…”

Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks

“…[15,16] proposed a network slicing technique and path similarity selection to optimize the use of radio resources for future Fifth-Generation (5G) communications. In order to explore the potential of Mode 4 in particular, the authors in [17][18][19] proposed solutions of adaptive power management mechanisms and discussed the effects of using message delivery time intervals. A report [20] describes the performance of Mode 4 in congested highway conditions.…”

Cluster-based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels

“…In addition to the complexity, the clustering and tracking processes conform with the corresponding physical observations. For instance, some MPCs coming from the same scatterer may form a cluster, and the MPC cluster will appear or disappear with the mobility of the transceiver, which is the essence of the tracking process [13]. Besides, the clustering focuses on the delay similarity in a specific distance, whereas the tracking process emphasizes the distance continuity and delay similarity.…”