Space clustering and identification based on full-domain channel characteristics for UAV communication networks

In order to improve the stability and reliability of unmanned aerial vehicle (UAV) communication networks, a channel subspace clustering and identification scheme based on channel full-domain characteristics is proposed in this paper. Firstly, the channels are characterized completely using distance domain, time delay domain, spatial domain and Doppler domain characteristics, and a channel subspace clustering method is proposed to form the channel subspace of channels with similar full domain characteristics as a basis for classification of UAV communication scenarios. Then, a channel subspace identification method based on back-propagation neural network is proposed, which can determine whether the new channel data belongs to the structure of the original channel subspace and channel full-domain characteristics are used as the feature tensor to improve the identification accuracy. Meanwhile, the influence of training data outliers is eliminated by calculating the distance between the channel and the center of the channel subspace, thus improving the robustness of identification method. Finally, 176 typical digital city scenarios are simulated in this paper by shooting and bounce ray/image mirror to obtain the full-domain characteristics of 176 000 channels and the corresponding channel state information, which are used to verify the accuracy of the clustering and identification method proposed in this paper. Simulation results show that the scenario identification method proposed in this paper can reduce the 176 identification targets of the traditional scenario identification method to 20, and the accuracy of the channel state characteristics in the channel subspace and identification method reaches 99% and 98.7%. Therefore, the method proposed in this paper can accurately identify the channel subspace in the UAV communication and provide a basis for UAV communication performance optimization.