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%.