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In this paper, to maximize the lifetime of drone swarms by resolving the battery exhaustion problems caused by undesired retransmissions, we propose a residual energy-aware online random access scheme (RE-ORA) by adjusting the packet transmission opportunities based on the residual energy of a drone in S-ALOHA-based swarming drone networks. We aim to improve the battery lifetime of the drone with the smallest energy among the drone swarms. In addition, we analyze the success, collision, and idle probabilities of the swarming drone networks. In particular, we analyze the intradrone swarm collision and interdrone swarm collision probabilities. Through intensive simulations, we show that the proposed RE-ORA scheme outperforms the conventional algorithm with respect to the average lifetime of drone swarms and successful packet transmission probability according to the number of drones and the residual energy.

To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.INDEX TERMS Unmanned aerial vehicle, optimal frequency reuse, transmit power control, energy efficiency, hierarchical multi-agent Q-learning, multi-UAV wireless network.

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