Dynamic Spectrum Collaboration for UAV Swarms Under Interference Conditions: A Multi-Agent Deep Reinforcement Learning Approach

This paper investigates the issue of spectrum collaboration under dynamic changes in the number of unmanned aerial vehicle (UAV) swarms under jamming conditions. We focus on the scenario where multiple UAV swarms conduct reconnaissance tasks, the head UAV (H-UAV) of each swarm relays the detected information gathered by the swarm back to the ground control station (GCS). To achieve spectrum collaboration among H-UAVs, first, we model the problem as an optimization that maximizes the sum of the achievable rates of H-UAVs to GCS. Then, we treat H-UAVs as agents and adopt the classic multi-agent proximal policy optimization (MAPPO) for centralized training of the agents. By setting the number of agents to a specific value, we make it applicable for distributed spectrum collaboration scenarios with dynamic changes in the number of UAV swarms. Finally, we measure the effectiveness of the algorithm by the spectrum conflict occurrences. Simulation results show that the proposed algorithm can significantly reduce the number of spectrum conflicts among H-UAVs, achieving effective spectrum collaboration under dynamic changes in the number of UAV swarms in jamming conditions.