Genetic Algorithms and Reinforcement Learning Approaches for Joint Placement, Power Control, and Activation of Flying Base Stations
This paper evaluates Genetic Algorithms and Proximal Policy Optimization-based Reinforcement Learning for the joint 3D placement, power control, and activation of Flying Base Stations, revealing that while Genetic Algorithms achieve higher user connectivity in interference-limited scenarios, the RL approach offers superior power efficiency and interference reduction.