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- W4386593900 abstract "UAV (Unmanned aerial vehicle) communication offers the possibility to establish the new net-works. To overcome the PE (pointing error) and beam misalignment of millimeter-wave massive MIMO (Multiple-in multiple-out)/FSO (Free Space Optical) caused by UAV jitter, a Tensor-train decomposition based hybrid beamforming for millimeter-wave massive MIMO/FSO in UAV with RIS (Reconfigurable Intelligence Surface) networks is investigated to improve the system spectral efficiency. Firstly, the high-dimensional channels of the RIS-assisted millimeter-wave massive MIMO/FSO in UAV are represented as the low-dimensional channels by Tensor-train decomposition. Secondly, the FSO PE caused by UAV jitter can be effectively solved by BIGRU (Bidirectional Gated Recurrent Unit)-attention neural network model. The fast-fading channels and Doppler shifts are estimated by the FCTPM (Fast Circulant Tensor Power Method) based on the Tensor-train decomposition. Finally, the RIS phase shift matrix is optimized by the SVD (Singular Value Decomposition). The Hybrid beamforming and RIS phase shift matrix are esti-mated by the low-complexity PE-AltMin (Phase Extraction Alternating Minimization) method to solve the beam misalignment. Simulation experiments demonstrate that the proposed method has higher spectrum utilization than other methods." @default.
- W4386593900 created "2023-09-12" @default.
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- W4386593900 date "2023-09-11" @default.
- W4386593900 modified "2023-10-17" @default.
- W4386593900 title "Tensor-Train Decomposition Based Hybrid Beamforming for Millimeter-Wave Massive MIMO/FSO in UAV with RIS Networks" @default.
- W4386593900 doi "https://doi.org/10.20944/preprints202309.0700.v1" @default.
- W4386593900 hasPublicationYear "2023" @default.
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