Resource Allocation Algorithm of AirComp Network Based on Multiple UAVs

被引：0
|
作者：
Tan L. [1 ]
Xu H. [1 ]
Liu Y.-F. [2 ]
Xia J.-M. [3 ]
机构：
[1] School of Computer, Nanjing University of Information Science and Technology, Jiangsu, Nanjing
[2] School of Software, Nanjing University of Information Science and Technology, Jiangsu, Nanjing
[3] School of Artificial Intelligence, Nanjing University of Information Science and Technology, Jiangsu, Nanjing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2023年 / 51卷 / 11期
基金：
中国国家自然科学基金;
关键词：
3D deployment; data aggregation; deep deterministic policy gradient algorithm; ground mobile sensors; over-the-air computation; unmanned aerial vehicle;
D O I：
10.12263/DZXB.20230513
中图分类号：
学科分类号：
摘要：
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. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：3070 / 3078
页数：8
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