In order to solve the problem of high computational complexity of traditional multi-user mmWave relay system beamforming methods， a Singular Value Decomposition （SVD） method based on Deep Learning （DL） was proposed to design hybrid beamforming for the optimization of the transmitter， relay and receiver. Firstly， DL method was used to design the beamforming matrix of transmitter and relay to maximize the achievable spectral efficiency. Then， the beamforming matrix of relay and receiver was designed to maximize the equivalent channel gain. Finally， a Minimum Mean Square Error （MMSE） filter was designed at the receiver to eliminate the inter-user interference. Theoretical analysis and simulation results show that compared with Alternating Maximization （AltMax） and the traditional SVD method， the hybrid beamforming method based on DL reduces the computational complexity by 12.5% and 23.44% respectively in the case of high dimensional channel matrix and many users， and has the spectral efficiency improved by 2.277% and 21.335% respectively with known Channel State Information （CSI）， and the spectral efficiency improved by 11.452% and 43.375% respectively with imperfect CSI.

Unmanned Aerial Vehicle （UAV） is flexible and easy to deploy， and can assist Mobile Edge Computing （MEC） to help wireless systems improve coverage and communication quality. However， there are challenges such as computational latency requirements and resource management in the research of UAV-assisted MEC systems. Aiming at the delay problem of UAV providing auxiliary calculation services to multiple ground terminals， a Twin Delayed Deep Deterministic policy gradient （TD3） based Task Offloading Algorithm for Delay Minimization （TD3-TOADM） was proposed. Firstly， the optimization problem was modeled as the problem of minimizing the maximum computational delay under energy constraints. Secondly， TD3-TOADM was used to jointly optimize terminal equipment scheduling， UAV trajectory and task offloading ratio to minimize the maximum computational delay. Simulation analysis results show that compared with the task offloading algorithms based on Actor-Critic （AC）， Deep Q-Network （DQN） and Deep Deterministic Policy Gradient （DDPG）， TD3-TOADM reduces the computational delay by more than 8.2%. It can be seen that TD3-TOADM algorithm has good convergence and robustness， and can obtain the optimal offloading strategy with low delay.

During the clustering process of high?dimensional data with feature missing， there are problems of the curse of dimensionality caused by data high dimension and the invalidity of effective distance calculation between samples caused by data feature missing. To resolve above issues， a K?Nearest Neighbor （KNN） imputation subspace clustering algorithm for high?dimensional data with feature missing was proposed， namely KISC. Firstly， the nearest neighbor relationship in the subspace of the high?dimensional data with feature missing was used to perform KNN imputation on the feature missing data in the original space. Then， multiple iterations of matrix decomposition and KNN imputation were used to obtain the final reliable subspace structure of the data， and the clustering analysis was performed in that obtained subspace structure. The clustering results in the original space of six image datasets show that the KISC algorithm has better performance than the comparison algorithm which clusters directly after interpolation， indicating that the subspace structure can identify the potential clustering structure of the data more easily and effectively； the clustering results in the subspace of six high?dimensional datasets shows that the KISC algorithm outperforms the comparison algorithm in all datasets， and has the optimal clustering Accuracy and Normalized Mutual Information （NMI） on most of the datasets. The KISC algorithm can deal with high?dimensional data with feature missing more effectively and improve the clustering performance of these data.