A Novel 3D Beam Domain Channel Model for UAV Massive MIMO Communications

Hengtai Chang, Cheng Xiang Wang, Ji Bian, Rui Feng, Yubei He, Yunfei Chen, El Hadi M. Aggoune

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.
Original languageEnglish (US)
Pages (from-to)5431-5445
Number of pages15
Issue number8
StatePublished - Aug 2023
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2023-09-04
Acknowledged KAUST grant number(s): ORA-2021-CRG10-4696
Acknowledgements: This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1801101; in part by the National Natural Science Foundation of China (NSFC) under Grant 61960206006 and Grant 62101311; in part by the Key Technologies Research and Development Program of Jiangsu (Prospective and Key Technologies for Industry) under Grant BE2022067, Grant BE2022067-3, and Grant BE2022067-1; in part by the Research Fund of National Mobile Communications Research Laboratory, Southeast University, under Grant 2021B02; in part by the European Union (EU), Horizon2020 (H2020), Research and Innovation Staff Exchange (RISE), Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment (TESTBED2) Project under Grant 872172; in part by the Distinguished Postdoctoral Program in Jiangsu; in part by King Abdullah University of Science and Technology Research Funding (KRF) under Award ORA-2021-CRG10-4696; and in part by the University of Tabuk, Saudi Arabia, under Grant S-1443-001.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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