Coverage Maximization for Air-and-Ground Cooperative Networks

How to extend the coverage area is vital for the six-generation mobile communication system (6G). In this paper, we consider a three-tier air-and-ground cooperative network, where some low-altitude relays (LRLs) are deployed to enhance the local coverage of the terrestrial base stations (TBSs) and some high-altitude base stations (HBSs) hover over the region to make up for the wide area coverage hole. To maximize the effective coverage user number, we formulate a joint LRL-position and HBS-trajectory optimization problem by considering the backhaul constraint of the LRLs and the dynamic feature of the HBSs. For the LRL-position optimization, we first remodel it as a maximum weighted independent set problem and then propose a graph-based algorithm to solve it. Furthermore, we combine the bionic algorithms with the deep neural network to devise an effective bionic-learning algorithm, which can quickly solve the complex HBS-trajectory optimization problem. Finally, simulation results demonstrate that our proposed algorithms can significantly improve the network coverage performance and reduce the computational complexity compared with the traditional optimization algorithms.