An ANN‐based channel modeling in 5G millimeter wave for a high‐voltage substation

Autor: Zihao Fu, Yu Zhang, Xiongwen Zhao, Fei Du, Suiyan Geng, Peng Qin, Zhenyu Zhou, Lei Zhang, Suhong Chen
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IET Communications, Vol 15, Iss 19, Pp 2425-2438 (2021)
Druh dokumentu: article
ISSN: 1751-8636
1751-8628
DOI: 10.1049/cmu2.12281
Popis: Abstract In this work, an artificial neural network (ANN) based time‐varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN‐based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high‐voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.
Databáze: Directory of Open Access Journals
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