期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 20, 期 1, 页码 535-548出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3026356
关键词
6G channel model; 3D space-time-frequency non-stationarity; correlated cluster based BD process method; K-Means clustering algorithm
资金
- National Key Research and Development Project, Ministry of Science and Technology [2017YFE0121400]
This paper proposes a novel general three-dimensional non-stationary model for 6G channels, which efficiently models the space-time-frequency non-stationarity through a correlated cluster-based BD method, taking into account interactions among space, time, and frequency. Simulation results and measurements demonstrate the accuracy of the proposed model.
In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., K-Means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.
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