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.

Aiming at the problem of the low pedestrian re-identification accuracy with disjoint multiple cameras in real environment caused by different camera scenes, perspectives, illuminations and other factors, a pedestrian re-identification feature extraction method based on attention mechanism was proposed. Firstly, the random erasure method was used to enhance the data of the input pedestrian image in order to improve the robustness of the network. Then, by constructing a from-top-to-bottom attention mechanism network, the saliency of the spatial pixel feature was enhanced, and the attention mechanism network was embedded in the ResNet50 network to extract the entire pedestrian salient features. Finally, the similarity measurement and ranking were performed on the entire salient features of pedestrians in order to obtain the accuracy of pedestrian re-identification. The pedestrian re-identification feature extraction method based on attention mechanism has Rank1 of 88.53% and mAP （mean Average Precision） of 70.70% on the Market1501 dataset, and has Rank1 of 77.33% and mAP of 59.47% on the DukeMTMC-reID dataset. The proposed method has significantly improved performance on the two major pedestrian re-identification datasets, and has certain application value.

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%.

Focusing on low the channel estimation accuracy is in virtual angular domain channel for Frequency Division Duplex based MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) systems, a new algorithm Based on Threshold Sparsity Adaptive Matching Pursuit (BT-SAMP) was proposed. The algorithm combined the atomic selection characteristics of BAOMP algorithm and the adaptive characteristics of Sparsity Adaptive Matching Pursuit (SAMP) algorithm. The Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) rule of the "adding atom" algorithm was used as the atomic selection preprocessing of the SAMP algorithm, the fixed atom was added by reasonable threshold, and then the step size of the SAMP algorithm was extended to find the maximum approximation coefficient of the channel matrix, which can improve the accuracy of SAMP algorithm and accelerate the convergence speed of the algorithm. The simulation results show that the channel estimation accuracy is improved compared with the SAMP algorithm in the case of low Signal-to-Noise Ratio (SNR), especially when the SNR is 0 to 10 dB, the estimation accuracy is improved by 4 dB, and the running time of the algorithm is reduced by about 61%.

Focused on the issue that the channel estimation accuracy of multi-cell multi-user MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) system was poor in the case of low Signal-to-Noise Ratio (SNR), a compressive sensing algorithm named Fruit Fly Stagewise Orthogonal Matching Pursuit (FF-StOMP) based on group intelligent search was proposed. Based on the Stagewise Orthogonal Matching Pursuit (StOMP) solution to the channel matrix parameters and the normalized minimum mean square error under different thresholds, the algorithm was used to search the minimum normalized mean square error and its corresponding threshold by the fruit fly optimization algorithm to achieve the adaptive parameter setting. The simulation results show that the channel estimation performance of FF-StOMP algorithm can be improved by 0.5 to 1 dB when the SNR is 0 to 10 dB compared with the StOMP algorithm. When the SNR is 11 to 20 dB, the channel estimation performance can be improved by 0.2 to 0.3 dB. When the number of cell users changes, the proposed algorithm can realize the adaptive channel estimation, which can effectively improve the channel estimation accuracy in the case of MASSIVE MIMO system with low SNR.