Deployment Optimization for Mobile Integrated Access and Backhaul Nodes in Air-Ground Integrated Networks

The use of mobile integrated access and backhaul (mIAB) is promising due to its high cost-effectiveness and fast deployment. However, the study of the deployment optimization for mIAB nodes is still incomplete, especially for air-ground integrated networks. To fill the gap, this paper aims to develop non-model-based deployment approaches for both unconstrained and constrained deployment problems. To this end, approaches with the zeroth order optimization techniques are proposed for solving deployment problems along with the use of deep neural network (DNN) surrogate models to help predicting network performance. In addition, effective training and design approaches for DNN surrogate models are provided by using data augmentation and active learning techniques. Simulation results show that our proposed approaches are effective and can outperform reference schemes.