当前位置：
X-MOL 学术
›
IEEE J. Sel. Area. Comm.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Playback of 5G and Beyond Measured MIMO Channels by an ANN-Based Modeling and Simulation Framework
IEEE Journal on Selected Areas in Communications ( IF 13.8 ) Pub Date : 2020-09-01 , DOI: 10.1109/jsac.2020.3000827 Xiongwen Zhao , Fei Du , Suiyan Geng , Zihao Fu , Zhongyu Wang , Yu Zhang , Zhenyu Zhou , Lei Zhang , Liuqing Yang
IEEE Journal on Selected Areas in Communications ( IF 13.8 ) Pub Date : 2020-09-01 , DOI: 10.1109/jsac.2020.3000827 Xiongwen Zhao , Fei Du , Suiyan Geng , Zihao Fu , Zhongyu Wang , Yu Zhang , Zhenyu Zhou , Lei Zhang , Liuqing Yang
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.
更新日期：2020-09-01
京公网安备 11010802027423号