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Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial cellular networks has emerged as an effective solution to provide coverage and complement communication services in a fast and cost-effective manner. The three-dimensional (3-D) trajectories of UAVs have a remarkable impact on the performance of such networks. On the other hand, UAVs are battery limited, and thus optimizing their energy consumption is of high importance. In this regard, we propose a novel trajectory design mechanism for rotary-wing UAV-BSs in 3-D space to improve the energy efficiency of the network. In this approach, UAVs aim at maximizing an objective function that captures the tradeoff between energy consumption and throughput, while satisfying their ground users’ quality-of-service requirements. Using reinforcement learning, we model our problem as a multiarmed bandit and propose an upper …