NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime Coverage
KAUST Grant NumberORA-2021-CRG10-4696
Permanent link to this recordhttp://hdl.handle.net/10754/689990
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AbstractCurrent fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.
CitationFang, X., Feng, W., Wang, Y., Chen, Y., Ge, N., Ding, Z., & Zhu, H. (2023). NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime Coverage. IEEE Transactions on Wireless Communications, 22(1), 138–152. https://doi.org/10.1109/twc.2022.3191719
SponsorsThis work was supported in part by the National Key Research and Development Program of China under Grant 2020YFA0711301; in part by the National Natural Science Foundation of China under Grant 61941104 and Grant 61922049; in part by the King Abdullah University of Science and Technology Research Funding (KRF) under Award ORA-2021-CRG10-4696; and in part by the Tsinghua University–China Mobile Communications Group Company Ltd., Joint Institute.