The deployment of unmanned aerial vehicles (UAVs) in many different settings has provided various solutions and strategies for networking paradigms. Therefore, it reduces the complexity of the developments for the existing problems, which otherwise require more sophisticated approaches. One of those existing problems is the unknown user locations in an infrastructure-less environment in which users cannot connect to any communication device or computation-providing server, which is essential to task offloading in order to achieve the required quality of service (QoS). Therefore, in this study, we investigate this problem thoroughly and propose a novel deep reinforcement learning (DRL) based scheme, DeepAir. DeepAir considers all of the necessary steps including sensing, localization, resource allocation, and multi-access edge computing (MEC) to achieve QoS requirements for the offloaded tasks without violating the maximum tolerable delay. To this end, we use two types of UAVs including detector UAVs, and serving UAVs. We utilize detector UAVs as DRL agents which ensure sensing, localization, and resource allocation. On the other hand, we utilize serving UAVs to provide MEC features. Our experiments show that DeepAir provides a high task success rate by deploying fewer detector UAVs in the environment, which includes different numbers of users and user attraction points, compared to benchmark methods.

本研究针对基础设施缺失环境中未知用户位置问题，提出了一种新颖的深度强化学习方案DeepAir。该方案整合了感知、定位、资源分配和多接入边缘计算等步骤，从而实现了在不违反最大容忍延迟的情况下满足任务卸载的服务质量要求。实验表明，DeepAir在使用更少的探测无人机的情况下，能够显著提高任务成功率。

DeepAir: 基于多智能体深度强化学习的未知用户位置问题方案