## 1 Introduction
By exploiting the angular sparsity of millimeter-wave channels, the continuously distributed angle of arrivals/departures (AoAs/AoDs) can be jointly estimated for hybrid beamforming [22]. A Hybrid Block Diagonalization (Hy-BD) method using a combiner to form a phase beam was obtained [26]. Digital block diagonalization with a large array gain was used to handle equivalent baseband channels. However, the available beamforming technology is limited in terms of performance improvements in the case of high-speed motion. The Reconfigurable Intelligent Surface (RIS) for beamforming in wireless communications was being considered for UAV communication systems in [27]. RIS needs to further consider the altitude, transmission distance, and multipath channel of the drone to plan the path. The RIS method of the Nonlinear Energy Harvesting Algorithm (REHS) was used to improve UAV communication performance by optimizing the phase of RIS reflective elements and parameters of nonlinear energy harvesting circuits. However, the coverage of indoor wireless networks lacks adaptability due to the mobility and time-varying channels of the drone. The RIS-based optimization and passive beamforming algorithm (RBOP) aims to realize joint trajectory design and passive beamforming in UAV communication, which can maximize the signal reception power of UAV communication systems [28]. If the target of hybrid beamforming is a partial connection, system performance will degrade. Multi-user beamforming of millimeter-wave massive MIMO for UAVs leads to high-dimensional operation [29; 30; 31; 32]. To maximize the spectral efficiency of the system, it is necessary to co-design RIS reflectance coefficients and hybrid beamforming. However, these algorithms are computationally expensive and inefficient. Although much research has focused on hybrid beamforming for UAV millimeter-wave massive MIMO/FSO, there are still some important scientific gaps that need to be filled. First, most studies do not consider the optimization of RIS phase-shift matrices, which is one of the key questions that needs to be addressed in this paper. Second, existing methods often face high computational complexity when dealing with massive MIMO systems. Finally, most studies do not consider optical PE problems caused by UAV jitter. In order to overcome the PE and beam misalignment of millimeter-wave large-scale multiple-input MIMO/FSO caused by UAV jitter, an millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is investigated in this paper. Firstly, the RIS-assisted millimeter-wave massive MIMO/FSO in the UAV channel model is established, and the high-dimensional channels of the RIS-assisted millimeter-wave massive MIMO/FSO of UAV are represented as the low-dimensional channels by tensor-train decomposition. Secondly, the problem of maximizing the spectral efficiency of the system is decomposed into two sub-problems of optimizing FSO optical PE and mixed beamforming matrix. The two-way gated cyclic unit attention neural network model can effectively solve the PE caused by UAV jitter. Fast fading channels and Doppler shifts are estimated by the Fast Cyclic Tensor Power Method (FCTPM) [33; 34]. The RIS phase shift matrix is refined using singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were determined by the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation experiments show that the proposed method is superior to other methods in terms of spectrum utilization.