Applying Coherent Ising Machines for Enhancing Communication Efficiency in Large-Scale UAV-Aided Networks

Unmanned aerial vehicles (UAVs) are used widely in wireless communications to enhance the service experience of mobile users on the ground. This study investigates a dynamic resource management problem in a large-scale UAV-aided wireless network, where multiple UAVs operate as aerial base stations to serve ground users. To enhance communication efficiency for all users in the presence of co-channel interference, we propose the use of an Ising model-based optimization method for fast and accurate UAV-user association and resource allocation. First, we formulate the dynamic user association and resource allocation problem as a combinatorial optimization problem. Subsequently, we transform the objective function in the formulated problem into the energy function of a quadratic unconstrained binary optimization (QUBO) model and applied coherent ising machine (CIM), which has been proven to be robust for solving dense Ising problems. We evaluate our proposed method through simulations and the real CIM for optimization. Additionally, we compared our method with simulated annealing, steepest descent, and exhaustive search. Performance evaluation results indicate that our proposed method is superior in terms of computational time and data rate.