Aiming at the problem that the data traffic in the intelligent wireless communication system presents complex characteristics such as burstiness and self-similarity, which leads to the low classification accuracy of the existing classification model for traffic, a data traffic classification method based on improved Harris Eagle algorithm combined with fuzzy C-means clustering is proposed. The method maps traffic samples to Harris Eagle individuals, finds the optimal position through multiple iterations of the algorithm, and uses this as the initial center point of the clustering algorithm to guide data traffic classification. The simulation shows that, compared with the traditional fuzzy clustering method, the clustering method based on the particle swarm algorithm and the gray wolf algorithm, the improved Harris Eagle combined with fuzzy clustering has better intra-class compactness and inter-class separation on the data traffic sample set. Meanwhile, the clustering accuracy and recall rate are both improved to about 90 percent.INDEX TERMS Data traffic classification, fuzzy clustering, Harris Hawk optimization, degree of compactness within a class, degree of separation between classes.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

A convolutional self-attention network-based channel state information reconstruction method is presented to address the issue of low reconstruction accuracy of channel state information in Multiple-Input Multiple-Output (MIMO) at a high compression rate. First, an encoder-decoder structure-based channel state information reconstruction model is built. The feature is extracted by the encoder’s convolutional network, and the information is compressed by adding an attention block. At the same time, the compressed information is nonuniform quantized to prevent the transmission process from using up too much bandwidth. A dequantization module and an attention block are added to the decoder to reduce the impact of noise on the matrix, converting the continuous value into a discrete value to increase reconstruction accuracy and using the long-time cosine annealing training approach. According to the simulation results, when compared to CsiNet, Lightweight CNN, CRNet, and CLNet, convergence speed is improved by 17.64%, indoor reconstruction precision is improved by an average of 37.4%, and outside reconstruction accuracy is improved by an average of 32.5% under all compressions.

In order to solve the problem of large channel state information (CSI) feedback overhead and low feedback accuracy in massive multiple-input multiple-output (MIMO) systems. We propose a CSI feedback method based on complex-valued convolutional neural networks to improve the representation capability of the network. In this method, a complex-valued encoder-decoder structure is constructed considering the fact that CSI exists in the form of complex numbers. We use complex convolutional downsampling (CCD) to extract CSI features in the encoder, and reconstruct the compressed CSI with high accuracy in the decoder by using a complex dense block (CDBlock). Simulation results show that the average accuracy is improved by 17.5% compared with several classical deep learning CSI feedback methods. Our proposed CSI feedback method has higher feedback accuracy and better system performance in massive MIMO systems.

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