Focused on the problems of high hardware cost and high system overhead in the millimeter-Wave Massive Multi-Input Multi-Output (mmWave Massive MIMO) system, an Orthogonal Matching Pursuit based on improved Intelligent Water Drop (IWD-OMP) hybrid precoding algorithm was proposed. Firstly, based on Orthogonal Match Pursuit (OMP) algorithm, the precoding matrix was solved. Secondly, the improved Intelligent Water Drop (IWD) algorithm was adopted to calculate the global optimal index vector in the matrix. Finally, the matrix solved by this method did not need to construct the candidate matrix in advance, which was able to save the system resources and reduce the complexity of matrix calculation. Experimental results demonstrate that when the number of transmitting antennas is 128 and the signal-to-noise ratio is 28 dB, compared with the OMP algorithm, the proposed method has the system achievable sum rate performance improved by about 7.71%, when the signal-to-noise ratio is 8 dB, the proposed method has the bit error rate reduced by about 19.77%. In addition, the proposed precoding algorithm has strong robustness to the imperfect Channel State Information (CSI) in the real channel environment. When the signal-to-noise ratio value is 28 dB, the proposed method has the system achievable sum rate decreased by about 1.08% for imperfect CSI compared with that for perfect CSI.

Aiming at the fact that the existing video person re-identification methods cannot effectively extract the spatiotemporal information between consecutive frames of the video, a person re-identification network based on non-local attention and multi-feature fusion was proposed to extract global and local representation features and time series information. Firstly, the non-local attention module was embedded to extract global features. Then, the multi-feature fusion was realized by extracting the low-level and middle-level features as well as the local features, so as to obtain the salient features of the person. Finally, the similarity measurement and sorting were performed to the person features in order to calculate the accuracy of video person re-identification. The proposed model has significantly improved performance compared to the existing Multi-scale 3D Convolution (M3D) and Learned Clip Similarity Aggregation (LCSA) models with the mean Average Precision (mAP) reached 81.4% and 93.4% respectively and the Rank-1 reached 88.7% and 95.3% respectively on the large datasets MARS and DukeMTMC-VideoReID. At the same time, the proposed model has the Rank-1 reached 94.8% on the small dataset PRID2011.

To solve the problems of high complexity of precoding and difficulty of linear matrix inversion in downlink Massive Multi-Input Multi-Output (Massive MIMO) system, a precoding algorithm based on low-complexity Symmetric Successive Over Relaxation Preconditioned Conjugate Gradient (SSOR-PCG) was proposed. Based on preconditioned Conjugate Gradient Precoding (PCG) algorithm, a Symmetric Successive Over Relaxation (SSOR) algorithm was used to preprocess the matrix to reduce its condition number, accelerating the convergence speed and the decreasing the complexity. Simulation results demonstrate that compared with PCG algorithm, the proposed algorithm has running time of around 88.93% shortened and achieves convergence when the Signal-to-Noise Ratio (SNR) is 26 dB. Furthermore, compared to zero-forcing precoding algorithm, the proposed algorithm requires only two iterations capacity-approaching performance,the overall complexity is reduced by one order of magnitude, and the bit error rate is decreased by about 49.94%.