Intelligent Beamforming for UAV-Assisted IIoT Based on Hypergraph Inspired Explainable Deep Learning

The development of industry is inseparable from the development of Industrial Internet of Things (IIoT), with the continuous development of artificial intelligence (AI), deep learning enabled intelligent communication has been widely studied. While, most existing neural networks for the current research often have poor performances on scalability and universality issues. In our previous work [1], a wireless communication scenario for UAV-assisted industrial IoT was considered, the beamforming problem in the above scenario was investigated and an unsupervised learning framework is introduced with mixed interference based graph neural network (MIGNN) to deal with the problem. However, it did not take into account the fact that different power constraints may have an impact on the performance of the model. This paper enriches the theoretical derivation and analysis, adds simulation experiments based on different power constraints and performance comparison analysis. Specifically, we conduct indepth analysis of the model's performance by adding two sets of comparative experiments, which can verify the stability and reliability of the model. Firstly, we constructed a system model for UAV assisted IIoT communication and modeled the beamforming problem. Secondly, we constructed heterogeneous graphs based on link state information. Then, a MIGNN model combining hypergraphs was proposed to obtain the optimal beamforming results. At last, from the experimental results, it can be verified that comparisons with traditional deep learning methods, the proposed one has stronger scalability and the results of the comparative experiments can successfully verifies the stability as well as the reliability of the model. Compared with our previous work, the simulation scenarios considered in this paper are more comprehensive and can be better applied to real wireless communication scenarios.