基于多无人机的空中计算网络资源分配算法

Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs

空中计算(over-the-Air Computation,AirComp)是一种有效提升分布式数据聚合效率的方法.现有研究大多采用单无人机(Unmanned Aerial Vehicle,UAV)方案,未考虑数据聚合质量和系统稳定性.为此,本文提出一种基于多UAV辅助的AirComp网络,旨在实现多个地面移动传感器(Ground Mobile Sensor,GMS)的高效聚合.为了改进数据采集质量并全面反映系统性能,本文设计了一个多约束优化问题,通过联合优化UAV-GMS关联、UAV三维(Three Dimensional,3D)部署、UAV去噪因子以及传输功率分配,以最大化系统的最小可达速率.针对多约束优化问题的非线性特征,本文提出一种AirComp网络下多UAV辅助的深度确定性策略梯度优化算法(DeepDeterministicPolicyGradient-basedoptimizationalgorithmformulti-UAVcooperationinAirCompnetwork,AirDDPG-UAV),用以协助多UAV在复杂环境下快速响应聚合任务.该算法利用深度强化学习的确定性策略对网络中的状态、行为和奖励进行优化,以最大化系统最小可达速率.数值结果显示,AirDDPG-UAV算法在保证较低的系统能耗和计算复杂度前提下,能够使系统最小可达速率提高15%,表明本文所提方案适用于分布式数据聚合,可以有效提高数据聚合效率.

Over-the-air computation(AirComp)is an effective method to improve the efficiency of distributed data aggregation,which can complete some task calculations while transmitting in the air.Most existing researches focus on the single unmanned aerial vehicle(UAV)scheme,without considering the quality of data aggregation and the stability of the system,making it unsuitable for practical AirComp environments.Therefore,this paper proposes an AirComp network based on multiple UAVs collaboration,which aims to achieve the efficient data aggregation for multiple ground mobile sensors(GMSs).In order to refine data acquisition and fully reflect system status,a multi-constraint non-convex optimization problem is constructed to jointly optimize UAV-GMS association,the three dimensional(3D)deployment of UAVs,UAV denoising factors,and transmission power allocation,aiming for maximizing the system's minimum achievable rate.Giving the nonlinear characteristics of multiple constraints optimization problems,a deep deterministic policy gradient-based optimization algorithm for multiple UAVs cooperation in AirComp network(AirDDPG-UAV)is proposed to assist UAVs rapidly responding to aggregation missions in complex environments.A deterministic policy in deep reinforcement is adopted to optimize the states,behaviors,and rewards of the AirComp network,aiming to maximize the minimal achievable rate.The numerical results show that the AirDDPG-UAV algorithm can significantly improve the system's minimum achievable rate by more than 15%compared to the benchmark methods,while ensuring suitable system energy consumption and computational complexity.The AirDDPG-UAV algorithm also obtains satisfactory results in optimizing the mean MSE,which illustrates our method has excellent performance in scaling signals and thus is helpful for fast data aggregation.The experiments indicate the proposed scheme is appropriate for the distributed data aggregation with low cost and can obviously improve the efficiency and stability of data aggregation.