Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision free path in mountainous environment, and the planned path is smooth and short. Compared with Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.INDEX TERMS Path planning,multi-UAV,sparrow search algorithm,bioinspired neural network,safe surface.

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.

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.

No abstract