Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile, allowing them to be strategically deployed based on ground user distribution and quality of service requirements, including coverage rate, system lifecycle, and user fairness. Owing to the limited battery capacity and coverage range of the UAVs, managing them to extend their operational lifecycle, ensure service fairness, and maintain a specific real-time coverage rate is challenging. Therefore, a multi-objective optimization problem with constrained Pareto dominance is formulated. Subsequently, a novel assisted deep reinforcement learning model is developed to maximize the minimum remaining energy while simultaneously considering user fairness and coverage-rate requirements. The particle swarm optimization algorithm is adopted to assist multi-agent cooperative deep reinforcement learning. Finally, the simulation results show that the proposed model outperforms the other popular methods in terms of user fairness, system lifecycle, coverage rate, and energy efficiency in the context of multi-objective, multi-agent cooperative coverage control deployment.

中文翻译：

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改进的基于 DRL 的节能无人机控制，以实现最大生命周期

作为机载基站（UAV-BS）运行的无人机（UAV）为地面用户提供高效的按需服务。无人机基站本质上是灵活和移动的，因此可以根据地面用户分布和服务质量要求（包括覆盖率、系统生命周期和用户公平性）进行战略部署。由于无人机的电池容量和覆盖范围有限，管理它们以延长其运行生命周期、确保服务公平性并保持特定的实时覆盖率具有挑战性。因此，提出了具有约束帕累托优势的多目标优化问题。随后，开发了一种新颖的辅助深度强化学习模型，以最大化最小剩余能量，同时考虑用户公平性和覆盖率要求。采用粒子群优化算法辅助多智能体协作深度强化学习。最后，仿真结果表明，在多目标、多智能体协作覆盖控制部署的背景下，该模型在用户公平性、系统生命周期、覆盖率和能源效率方面优于其他流行方法。