A Hierarchical MAFDRL-Based Resource Allocation and Incentive Mechanism for TN-NTN in 6G Networks

To address the limitations of existing wireless networks for demanding applications like brain-computer interfaces and intelligent transportation systems, we propose an advanced framework for joint resource allocation and task offloading across integrated terrestrial and non-terrestrial networks (TN-NTN). This framework utilizes multiple layers, including ground users, UAVs, HAPs, and satellites, to improve service quality and immersive experiences, particularly in scenarios like Metaverse applications. Ground users request resources, while UAVs and HAPs serve as resource providers, and satellites ensure reliable communication during emergencies. A double auction-based incentive scheme is employed in which operators control UAV and HAP resources to maximize utility, and users aim to minimize computation costs and protect data privacy. To handle the complexity of the operator-user interaction, which results in an NP-hard optimization problem, we applied a hierarchical multi-agent federated deep reinforcement learning (FeDRL) approach. Our simulation results demonstrate that the FeDRL algorithm significantly improves social welfare by 6.38%, 17.43%, and 28.73% over modified MADDPG, FRL, and DDPG algorithms, respectively.