Spectrum Sharing in UAV-Assisted HetNet Based on CMB-AM Multi-Agent Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) assisted heterogeneous network (HetNet) is a promising solution to outdoor hotspots. This poster proposes a coordination-mini-batch with action mask (CMB-AM) multi-agent deep reinforcement learning (DRL) based resource allocation scheme for uplink spectrum sharing in a UAV-assisted two-tier heterogeneous network (HetNet). We utilize a centralized training and distributed execution mechanism and consider the correlation among actions to make the agents collaborate implicitly. Due to independent state and collective reward design, our resource allocation scheme can be robust and scalable to the varying number of agents. Evaluation shows that the proposed scheme achieves better performance on the aspects of sum capacity, model applicability, and training stability than other baseline schemes.