Resource Allocation for Aerial Assisted Digital Twin Edge Mobile Network

In the context of the 5G/6G mobile network, high levels of requirements such as ultra-high data transmission rate, support for the high mobility node and seamless connection need to be handled. Additionally, ensuring user quality of service (QoS) in high-density and high-traffic mobile networks presents a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as key components in providing flexible assistance in aerial spaces. To further enhance the network performance in dynamic and heterogeneous environments, an intelligent resource allocation strategy with low communication overhead is essential. In this paper, we construct a UAV-assisted mobile network to provide efficient communication for all mobile users in high-density and high-traffic environments, at the same time, a digital twin-empowered dynamic resource allocation strategy based on online training with low communication overhead is proposed. Our proposal employs digital twin-empowered multi-task learning to meet various resource allocation requirements for different node types. Moreover, we propose a deep-Q network-based reinforcement learning mechanism with experience replay memory to execute resource allocation decisions based on evaluated rewards. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.