期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 38, 期 9, 页码 1945-1954出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3000827
关键词
Position or time-varying channel; artificial neural network; channel modeling and simulation; virtual array; measurement; millimeter wave; 5G and beyond
资金
- National Nature Science Foundation of China (NSFC) [61771194, 61931001]
- Science and Technology Project of State Grid Corporation of China [SGSDDK00KJJS1900405]
- Key Program of Beijing Municipal Natural Science Foundation [17L20052]
In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.
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