Teppei Otsuka

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 proposed the use of an Ising model-based optimization method for fast and accurate UAV-user association and resource allocation. First, we formulated the dynamic user association and resource allocation problem as a combinatorial optimization problem. Subsequently, We transformed 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 elevated our proposed method through simulations using CIM for optimization. Additionally, we compared our method with simulated annealing, steepest descent, and exhaustive search. Simulation results indicate that our proposed method is superior in terms of computational time and data rate.

Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization problem is NP-hard, and obtaining a good approximation of a solution with a low computational complexity algorithm is not easy. To overcome this problem, we propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. The CIM is an Ising system that can deliver fair approximate solutions to combinatorial optimization problems at high speed (millisecond order) by operating optimization algorithms based on mutually connected photonic neural networks. The performance of our proposed method was evaluated using a simulation model of the CIM. We compared the performance of our proposed method to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme. Simulation results indicate that our proposed method is superior in terms of speed and the attained optimal solutions.